draft-ietf-rmcat-video-traffic-model-06.txt   draft-ietf-rmcat-video-traffic-model-07.txt 
Network Working Group X. Zhu Network Working Group X. Zhu
Internet-Draft S. Mena Internet-Draft S. Mena
Intended status: Informational Cisco Systems Intended status: Informational Cisco Systems
Expires: May 7, 2019 Z. Sarker Expires: August 23, 2019 Z. Sarker
Ericsson AB Ericsson AB
November 3, 2018 February 19, 2019
Video Traffic Models for RTP Congestion Control Evaluations Video Traffic Models for RTP Congestion Control Evaluations
draft-ietf-rmcat-video-traffic-model-06 draft-ietf-rmcat-video-traffic-model-07
Abstract Abstract
This document describes two reference video traffic models for This document describes two reference video traffic models for
evaluating RTP congestion control algorithms. The first model evaluating RTP congestion control algorithms. The first model
statistically characterizes the behavior of a live video encoder in statistically characterizes the behavior of a live video encoder in
response to changing requests on target video rate. The second model response to changing requests on the target video rate. The second
is trace-driven, and emulates the output of actual encoded video model is trace-driven and emulates the output of actual encoded video
frame sizes from a high-resolution test sequence. Both models are frame sizes from a high-resolution test sequence. Both models are
designed to strike a balance between simplicity, repeatability, and designed to strike a balance between simplicity, repeatability, and
authenticity in modeling the interactions between a live video authenticity in modeling the interactions between a live video
traffic source and the congestion control module. Finally, the traffic source and the congestion control module. Finally, the
document describes how both approaches can be combined into a hybrid document describes how both approaches can be combined into a hybrid
model. model.
Status of This Memo Status of This Memo
This Internet-Draft is submitted in full conformance with the This Internet-Draft is submitted in full conformance with the
skipping to change at page 1, line 42 skipping to change at page 1, line 42
Internet-Drafts are working documents of the Internet Engineering Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet- working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/. Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress." material or to cite them other than as "work in progress."
This Internet-Draft will expire on May 7, 2019. This Internet-Draft will expire on August 23, 2019.
Copyright Notice Copyright Notice
Copyright (c) 2018 IETF Trust and the persons identified as the Copyright (c) 2019 IETF Trust and the persons identified as the
document authors. All rights reserved. document authors. All rights reserved.
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Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3 2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Desired Behavior of A Synthetic Video Traffic Model . . . . . 3 3. Desired Behavior of A Synthetic Video Traffic Model . . . . . 3
4. Interactions Between Synthetic Video Traffic Source and 4. Interactions Between Synthetic Video Traffic Source and
Other Components at the Sender . . . . . . . . . . . . . . . 4 Other Components at the Sender . . . . . . . . . . . . . . . 5
5. A Statistical Reference Model . . . . . . . . . . . . . . . . 6 5. A Statistical Reference Model . . . . . . . . . . . . . . . . 6
5.1. Time-damped response to target rate update . . . . . . . 7 5.1. Time-damped response to target rate update . . . . . . . 7
5.2. Temporary burst and oscillation during the transient 5.2. Temporary burst and oscillation during the transient
period . . . . . . . . . . . . . . . . . . . . . . . . . 8 period . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.3. Output rate fluctuation at steady state . . . . . . . . . 8 5.3. Output rate fluctuation at steady state . . . . . . . . . 8
5.4. Rate range limit imposed by video content . . . . . . . . 9 5.4. Rate range limit imposed by video content . . . . . . . . 9
6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 9 6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 9
6.1. Choosing the video sequence and generating the traces . . 10 6.1. Choosing the video sequence and generating the traces . . 10
6.2. Using the traces in the synthetic codec . . . . . . . . . 11 6.2. Using the traces in the synthetic codec . . . . . . . . . 11
6.2.1. Main algorithm . . . . . . . . . . . . . . . . . . . 11 6.2.1. Main algorithm . . . . . . . . . . . . . . . . . . . 11
6.2.2. Notes to the main algorithm . . . . . . . . . . . . . 13 6.2.2. Notes to the main algorithm . . . . . . . . . . . . . 13
6.3. Varying frame rate and resolution . . . . . . . . . . . . 13 6.3. Varying frame rate and resolution . . . . . . . . . . . . 14
7. Combining The Two Models . . . . . . . . . . . . . . . . . . 14 7. Combining The Two Models . . . . . . . . . . . . . . . . . . 14
8. Implementation Status . . . . . . . . . . . . . . . . . . . . 15 8. Implementation Status . . . . . . . . . . . . . . . . . . . . 16
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16 9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16
10. Security Considerations . . . . . . . . . . . . . . . . . . . 16 10. Security Considerations . . . . . . . . . . . . . . . . . . . 16
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 16 11. References . . . . . . . . . . . . . . . . . . . . . . . . . 16
11.1. Normative References . . . . . . . . . . . . . . . . . . 16 11.1. Normative References . . . . . . . . . . . . . . . . . . 16
11.2. Informative References . . . . . . . . . . . . . . . . . 16 11.2. Informative References . . . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 17 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 17
1. Introduction 1. Introduction
When evaluating candidate congestion control algorithms designed for When evaluating candidate congestion control algorithms designed for
real-time interactive media, it is important to account for the real-time interactive media, it is important to account for the
characteristics of traffic patterns generated from a live video characteristics of traffic patterns generated from a live video
encoder. Unlike synthetic traffic sources that can conform perfectly encoder. Unlike synthetic traffic sources that can conform perfectly
to the rate changing requests from the congestion control module, a to the rate changing requests from the congestion control module, a
live video encoder can be sluggish in reacting to such changes. live video encoder can be sluggish in reacting to such changes. The
Output rate of a live video encoder also typically deviates from the output rate of a live video encoder also typically deviates from the
target rate due to uncertainties in the encoder rate control process. target rate due to uncertainties in the encoder rate control process.
Consequently, end-to-end delay and loss performance of a real-time Consequently, end-to-end delay and loss performance of a real-time
media flow can be further impacted by rate variations introduced by media flow can be further impacted by rate variations introduced by
the live encoder. the live encoder.
On the other hand, evaluation results of a candidate RTP congestion On the other hand, evaluation results of a candidate RTP congestion
control algorithm should mostly reflect performance of the congestion control algorithm should mostly reflect the performance of the
control module, and somewhat decouple from peculiarities of any congestion control module and somewhat decouple from peculiarities of
specific video codec. It is also desirable that evaluation tests are any specific video codec. It is also desirable that evaluation tests
repeatable, and be easily duplicated across different candidate are repeatable, and be easily duplicated across different candidate
algorithms. algorithms.
One way to strike a balance between the above considerations is to One way to strike a balance between the above considerations is to
evaluate congestion control algorithms using a synthetic video evaluate congestion control algorithms using a synthetic video
traffic source model that captures key characteristics of the traffic source model that captures key characteristics of the
behavior of a live video encoder. To this end, this draft presents behavior of a live video encoder. The synthetic traffic model should
two reference models. The first is based on statistical modeling; also contain tunable parameters so that it can be flexibly adjusted
the second is trace-driven. The draft also discusses the pros and to reflect the wide variations in real-world live video encoder
cons of each approach, as well as how both approaches can be combined behaviors. To this end, this draft presents two reference models.
into a hybrid model. The first is based on statistical modeling. The second is driven by
frame size and interval traces recorded from a real-world encoder.
The draft also discusses the pros and cons of each approach, as well
as how both approaches can be combined into a hybrid model.
2. Terminology 2. Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP "OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119] [RFC8174] when, and only when, they appear in all 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here. capitals, as shown here.
3. Desired Behavior of A Synthetic Video Traffic Model 3. Desired Behavior of A Synthetic Video Traffic Model
A live video encoder employs encoder rate control to meet a target A live video encoder employs encoder rate control to meet a target
rate by varying its encoding parameters, such as quantization step rate by varying its encoding parameters, such as quantization step
size, frame rate, and picture resolution, based on its estimate of size, frame rate, and picture resolution, based on its estimate of
the video content (e.g., motion and scene complexity). In practice, the video content (e.g., motion and scene complexity). In practice,
however, several factors prevent the output video rate from perfectly however, several factors prevent the output video rate from perfectly
conforming to the input target rate. conforming to the input target rate.
Due to uncertainties in the captured video scene, the output rate Due to uncertainties in the captured video scene, the output rate
typically deviates from the specified target. In the presence of a typically deviates from the specified target. In the presence of a
significant change in target rate, the encoder output frame sizes significant change in target rate, the encoder's output frame sizes
sometimes fluctuates for a short, transient period of time before the sometimes fluctuate for a short, transient period of time before the
output rate converges to the new target. Finally, while most of the output rate converges to the new target. Finally, while most of the
frames in a live session are encoded in predictive mode, the encoder frames in a live session are encoded in predictive mode (i.e.,
can occasionally generate a large intra-coded frame (or a frame P-frames in [H264]), the encoder can occasionally generate a large
partially containing intra-coded blocks) in an attempt to recover intra-coded frame (i.e., I-frame as defined in [H264]) or a frame
from losses, to re-sync with the receiver, or during the transient partially containing intra-coded blocks in an attempt to recover from
period of responding to target rate or spatial resolution changes. losses, to re-sync with the receiver, or during the transient period
of responding to target rate or spatial resolution changes.
Hence, a synthetic video source should have the following Hence, a synthetic video source should have the following
capabilities: capabilities:
o To change bitrate. This includes ability to change framerate and/ o To change bitrate. This includes the ability to change framerate
or spatial resolution, or to skip frames when required. and/or spatial resolution or to skip frames upon request.
o To fluctuate around the target bitrate specified by the congestion o To fluctuate around the target bitrate specified by the congestion
control module. control module.
o To show a delay in convergence to the target bitrate. o To show a delay in convergence to the target bitrate.
o To generate intra-coded or repair frames on demand. o To generate intra-coded or repair frames on demand.
While there exist many different approaches in developing a synthetic While there exist many different approaches in developing a synthetic
video traffic model, it is desirable that the outcome follows a few video traffic model, it is desirable that the outcome follows a few
skipping to change at page 4, line 37 skipping to change at page 4, line 40
instances generated by the model should mimic the temporal pattern instances generated by the model should mimic the temporal pattern
of those from a real video encoder. of those from a real video encoder.
o Statistical resemblance: The synthetic traffic source should match o Statistical resemblance: The synthetic traffic source should match
the outcome of the real video encoder in terms of statistical the outcome of the real video encoder in terms of statistical
characteristics, such as the mean, variance, peak, and characteristics, such as the mean, variance, peak, and
autocorrelation coefficients of the bitrate. It is also important autocorrelation coefficients of the bitrate. It is also important
that the statistical resemblance should hold across different time that the statistical resemblance should hold across different time
scales, ranging from tens of milliseconds to sub-seconds. scales, ranging from tens of milliseconds to sub-seconds.
o Wide range of coverage: The model should be easily configurable to o A wide range of coverage: The model should be easily configurable
cover a wide range of codec behaviors (e.g., with either fast or to cover a wide range of codec behaviors (e.g., with either fast
slow reaction time in live encoder rate control) and video content or slow reaction time in live encoder rate control) and video
variations (e.g., ranging from high-motion to low-motion). content variations (e.g., ranging from high to low motion).
These distinct behavior features can be characterized via simple These distinct behavior features can be characterized via simple
statistical modelling, or a trace-driven approach. Section 5 and statistical modeling or a trace-driven approach. Section 5 and
Section 6 provide an example of each approach, respectively. Section 6 provide an example of each approach, respectively.
Section 7 discusses how both models can be combined together. Section 7 discusses how both models can be combined together.
4. Interactions Between Synthetic Video Traffic Source and Other 4. Interactions Between Synthetic Video Traffic Source and Other
Components at the Sender Components at the Sender
Figure 1 depicts the interactions of the synthetic video traffic Figure 1 depicts the interactions of the synthetic video traffic
source with other components at the sender, such as the application, source with other components at the sender, such as the application,
the congestion control module, the media packet transport module, the congestion control module, the media packet transport module,
etc. Both reference models --- as described later in Section 5 and etc. Both reference models --- as described later in Section 5 and
skipping to change at page 5, line 20 skipping to change at page 5, line 27
the network. During the lifetime of a video transmission session, the network. During the lifetime of a video transmission session,
the synthetic video source will typically be required to adapt its the synthetic video source will typically be required to adapt its
encoding bitrate, and sometimes the spatial resolution and frame encoding bitrate, and sometimes the spatial resolution and frame
rate. rate.
In this model, the synthetic video source module has a group of In this model, the synthetic video source module has a group of
incoming and outgoing interface calls that allow for interaction with incoming and outgoing interface calls that allow for interaction with
other modules. The following are some of the possible incoming other modules. The following are some of the possible incoming
interface calls --- marked as (a) in Figure 1 --- that the synthetic interface calls --- marked as (a) in Figure 1 --- that the synthetic
video traffic source may accept. The list is not exhaustive and can video traffic source may accept. The list is not exhaustive and can
be complemented by other interface calls if deemed necessary. be complemented by other interface calls if necessary.
o Target rate R_v: target rate request, typically calculated by the o Target bitrate R_v: target bitrate request measured in bits per
congestion control module and updated dynamically over time. second (bps). Typically, the congestion control module calculates
the target bitrate and updates it dynamically over time.
Depending on the congestion control algorithm in use, the update Depending on the congestion control algorithm in use, the update
requests can either be periodic (e.g., once per second), or on- requests can either be periodic (e.g., once per second), or on-
demand (e.g., only when a drastic bandwidth change over the demand (e.g., only when a drastic bandwidth change over the
network is observed). network is observed).
o Target frame rate FPS: the instantaneous frame rate measured in o Target frame rate FPS: the instantaneous frame rate measured in
frames-per-second at a given time. This depends on the native frames-per-second at a given time. This depends on the native
camera capture frame rate as well as the target/preferred frame camera capture frame rate as well as the target/preferred frame
rate configured by the application or user. rate configured by the application or user.
o Target frame resolution XY: the 2-dimensional vector indicating o Target frame resolution XY: the 2-dimensional vector indicating
the preferred frame resolution in pixels. Several factors govern the preferred frame resolution in pixels. Several factors govern
the resolution requested to the synthetic video source over time. the resolution requested to the synthetic video source over time.
Examples of such factors include the capturing resolution of the Examples of such factors include the capturing resolution of the
native camera and the display size of the destination screen. The native camera and the display size of the destination screen. The
target frame resolution also depends on the current target rate target frame resolution also depends on the current target bitrate
R_v, since very small resolutions do not make sense with very high R_v, since it does not make sense to pair very low spatial
bitrates, and vice-versa. resolutions with very high bitrates, and vice-versa.
o Instant frame skipping: the request to skip the encoding of one or o Instant frame skipping: the request to skip the encoding of one or
several captured video frames, for instance when a drastic several captured video frames, for instance when a drastic
decrease in available network bandwidth is detected. decrease in available network bandwidth is detected.
o On-demand generation of intra (I) frame: the request to encode o On-demand generation of intra (I) frame: the request to encode
another I frame to avoid further error propagation at the another I frame to avoid further error propagation at the receiver
receiver, if severe packet losses are observed. This request when severe packet losses are observed. This request typically
typically comes from the error control module. comes from the error control module. It can be initiated either
by the sender or by the receiver via Full Intra Request (FIR)
messages as defined in [RFC5104].
An example of outgoing interface call --- marked as (b) in Figure 1 An example of outgoing interface call --- marked as (b) in Figure 1
--- is the rate range [R_min, R_max]. Here, R_min and R_max are --- is the rate range [R_min, R_max]. Here, R_min and R_max are
meant to capture the dynamic rate range and actual live video encoder meant to capture the dynamic rate range and actual live video encoder
is capable of generating given the input video content. This is capable of generating given the input video content. This
typically depends on the video content complexity and/or display type typically depends on the video content complexity and/or display type
(e.g., higher R_max for video contents with higher motion complexity, (e.g., higher R_max for video contents with higher motion complexity,
or for displays of higher resolution). Therefore, these values will or for displays of higher resolution). Therefore, these values will
not change with R_v, but may change over time if the content is not change with R_v but may change over time if the content is
changing. changing.
+-------------+ +-------------+
| | encoded video | | dummy encoded
| Synthetic | frames | Synthetic | video frames
| Video | --------------> | Video | -------------->
| Source | | Source |
| | | |
+--------+----+ +--------+----+
/|\ | /|\ |
| | | |
-------------------+ +--------------------> -------------------+ +-------------------->
interface from interface to interface from interface to
other modules (a) other modules (b) other modules (a) other modules (b)
skipping to change at page 7, line 8 skipping to change at page 7, line 8
This section describes one simple statistical model of the live video This section describes one simple statistical model of the live video
encoder traffic source. Figure 2 summarizes the list of tunable encoder traffic source. Figure 2 summarizes the list of tunable
parameters in this statistical model. A more comprehensive survey of parameters in this statistical model. A more comprehensive survey of
popular methods for modeling video traffic source behavior can be popular methods for modeling video traffic source behavior can be
found in [Tanwir2013]. found in [Tanwir2013].
+===========+====================================+================+ +===========+====================================+================+
| Notation | Parameter Name | Example Value | | Notation | Parameter Name | Example Value |
+===========+====================================+================+ +===========+====================================+================+
| R_v | Target rate request | 1 Mbps | | R_v | Target bitrate request | 1 Mbps |
+-----------+------------------------------------+----------------+ +-----------+------------------------------------+----------------+
| FPS | Target frame rate | 30 Hz | | FPS | Target frame rate | 30 Hz |
+-----------+------------------------------------+----------------+ +-----------+------------------------------------+----------------+
| tau_v | Encoder reaction latency | 0.2 s | | tau_v | Encoder reaction latency | 0.2 s |
+-----------+------------------------------------+----------------+ +-----------+------------------------------------+----------------+
| K_d | Burst duration of the transient | 8 frames | | K_d | Burst duration of the transient | 8 frames |
| | period | | | | period | |
+-----------+------------------------------------+----------------+ +-----------+------------------------------------+----------------+
| K_B | Burst frame size during the | 13.5 KBytes* | | K_B | Burst frame size during the | 13.5 KBytes* |
| | transient period | | | | transient period | |
skipping to change at page 7, line 49 skipping to change at page 7, line 49
+===========+====================================+================+ +===========+====================================+================+
* Example value of K_B for a video stream encoded at 720p and * Example value of K_B for a video stream encoded at 720p and
30 frames per second, using H.264/AVC encoder. 30 frames per second, using H.264/AVC encoder.
Figure 2: List of tunable parameters in a statistical video traffic Figure 2: List of tunable parameters in a statistical video traffic
source model. source model.
5.1. Time-damped response to target rate update 5.1. Time-damped response to target rate update
While the congestion control module can update its target rate While the congestion control module can update its target bitrate
request R_v at any time, the statistical model dictates that the request R_v at any time, the statistical model dictates that the
encoder will only react to such changes tau_v seconds after a encoder will only react to such changes tau_v seconds after a
previous rate transition. In other words, when the encoder has previous rate transition. In other words, when the encoder has
reacted to a rate change request at time t, it will simply ignore all reacted to a rate change request at time t, it will simply ignore all
subsequent rate change requests until time t+tau_v. subsequent rate change requests until time t+tau_v.
5.2. Temporary burst and oscillation during the transient period 5.2. Temporary burst and oscillation during the transient period
The output rate R_o during the period [t, t+tau_v] is considered to The output bitrate R_o during the period [t, t+tau_v] is considered
be in a transient state. Based on observations from video encoder to be in a transient state when reacting to abrupt changes in target
output data, the encoder reaction to a new target rate request can be rate. Based on observations from video encoder output data, the
characterized by high variation in output frame sizes. It is assumed encoder reaction to a new target bitrate request can be characterized
in the model that the overall average output rate R_o during this by high variations in output frame sizes. It is assumed in the model
transient period matches the target rate R_v. Consequently, the that the overall average output bitrate R_o during this transient
occasional burst of large frames are followed by smaller-than-average period matches the target bitrate R_v. Consequently, the occasional
encoded frames. burst of large frames is followed by smaller-than-average encoded
frames.
This temporary burst is characterized by two parameters: This temporary burst is characterized by two parameters:
o burst duration K_d: number of frames in the burst event; and o burst duration K_d: number of frames in the burst event; and
o burst frame size K_B: size of the initial burst frame which is o burst frame size K_B: size of the initial burst frame which is
typically significantly larger than average frame size at steady typically significantly larger than average frame size at steady
state. state.
It can be noted that these burst parameters can also be used to mimic It can be noted that these burst parameters can also be used to mimic
the insertion of a large on-demand I frame in the presence of severe the insertion of a large on-demand I frame in the presence of severe
packet losses. The values of K_d and K_B typically depend on the packet losses. The values of K_d and K_B typically depend on the
type of video codec, spatial and temporal resolution of the encoded type of video codec, spatial and temporal resolution of the encoded
stream, as well as the video content activity level. stream, as well as the video content activity level.
5.3. Output rate fluctuation at steady state 5.3. Output rate fluctuation at steady state
The output rate R_o during steady state is modelled as randomly The output bitrate R_o during steady state is modeled as randomly
fluctuating around the target rate R_v. The output traffic can be fluctuating around the target bitrate R_v. The output traffic can be
characterized as the combination of two random processes denoting the characterized as the combination of two random processes denoting the
frame interval t and output frame size B over time. These two random frame interval t and output frame size B over time, as the two major
processes capture two sources of variations in the encoder output: sources of variations in the encoder output. For simplicity, the
deviations of t and B from their respective reference levels are
modeled as independent and identically distributed (i.i.d) random
variables following the Laplacian distribution [Papoulis]. More
specifically:
o Fluctuations in frame interval: the intervals between adjacent o Fluctuations in frame interval: the intervals between adjacent
frames have been observed to fluctuate around the reference frames have been observed to fluctuate around the reference
interval of t0 = 1/FPS. Deviations in normalized frame interval interval of t0 = 1/FPS. Deviations in normalized frame interval
DELTA_t = (t-t0)/t0 can be modelled by a zero-mean Laplacian DELTA_t = (t-t0)/t0 can be modeled by a zero-mean Laplacian
distribution with scaling parameter SCALE_t. The value of SCALE_t distribution with scaling parameter SCALE_t. The value of SCALE_t
dictates the "width" of the Laplacian distribution and therefore dictates the "width" of the Laplacian distribution and therefore
the amount of fluctuations in actual frame intervals (t) with the amount of fluctuation in actual frame intervals (t) with
respect to the reference frame interval t0. respect to the reference frame interval t0.
o Fluctuations in frame size: size of the output encoded frames also o Fluctuations in frame size: the output encoded frame sizes also
tend to fluctuate around the reference frame size B0=R_v/8/FPS. tend to fluctuate around the reference frame size B0=R_v/8/FPS.
Likewise, deviations in the normalized frame size DELTA_B = Likewise, deviations in the normalized frame size DELTA_B =
(B-B0)/B0 can be modelled by a zero-mean Laplacian distribution (B-B0)/B0 can be modeled by a zero-mean Laplacian distribution
with scaling parameter SCALE_B. The value of SCALE_B dictates the with scaling parameter SCALE_B. The value of SCALE_B dictates the
"width" of this second Laplacian distribution and correspondingly "width" of this second Laplacian distribution and correspondingly
the amount of fluctuations in output frame sizes (B) with respect the amount of fluctuations in output frame sizes (B) with respect
to the reference target B0. to the reference target B0.
Both values of SCALE_t and SCALE_B can be obtained via parameter Both values of SCALE_t and SCALE_B can be obtained via parameter
fitting from empirical data captured for a given video encoder. fitting from empirical data captured for a given video encoder.
Example values are listed in Figure 2 based on empirical data Example values are listed in Figure 2 based on empirical data
presented in [IETF-Interim]. presented in [IETF-Interim].
5.4. Rate range limit imposed by video content 5.4. Rate range limit imposed by video content
The output rate R_o is further clipped within the dynamic range The output bitrate R_o is further clipped within the dynamic range
[R_min, R_max], which in reality are dictated by scene and motion [R_min, R_max], which in reality are dictated by scene and motion
complexity of the captured video content. In the proposed complexity of the captured video content. In the proposed
statistical model, these parameters are specified by the application. statistical model, these parameters are specified by the application.
6. A Trace-Driven Model 6. A Trace-Driven Model
The second approach for modelling a video traffic source is trace- The second approach for modeling a video traffic source is trace-
driven. This can be achieved by running an actual live video encoder driven. This can be achieved by running an actual live video encoder
on a set of chosen raw video sequences and using the encoder's output on a set of chosen raw video sequences and using the encoder's output
traces for constructing a synthetic video source. With this traces for constructing a synthetic video source. With this
approach, the recorded video traces naturally exhibit temporal approach, the recorded video traces naturally exhibit temporal
fluctuations around a given target rate request R_v from the fluctuations around a given target bitrate request R_v from the
congestion control module. congestion control module.
The following list summarizes the main steps of this approach: The following list summarizes the main steps of this approach:
1. Choose one or more representative raw video sequences. 1. Choose one or more representative raw video sequences.
2. Encode the sequence(s) using an actual live video encoder. 2. Encode the sequence(s) using an actual live video encoder.
Repeat the process for a number of bitrates. Keep only the Repeat the process for a number of bitrates. Keep only the
sequence of frame sizes for each bitrate. sequence of frame sizes for each bitrate.
skipping to change at page 10, line 16 skipping to change at page 10, line 21
Section 6.2 elaborates on the remaining two steps (4-5). Finally, Section 6.2 elaborates on the remaining two steps (4-5). Finally,
Section 6.3 briefly discusses the possibility to extend the trace- Section 6.3 briefly discusses the possibility to extend the trace-
driven model for supporting time-varying frame rate and/or time- driven model for supporting time-varying frame rate and/or time-
varying frame resolution. varying frame resolution.
6.1. Choosing the video sequence and generating the traces 6.1. Choosing the video sequence and generating the traces
The first step is a careful choice of a set of video sequences that The first step is a careful choice of a set of video sequences that
are representative of the target use cases for the video traffic are representative of the target use cases for the video traffic
model. For the example use case of interactive video conferencing, model. For the example use case of interactive video conferencing,
it is recommended to choose a low-motion sequence that resembles a it is recommended to choose a sequence with content that resembles a
"talking head", e.g. from a news broadcast or recording of an actual "talking head", e.g. from a news broadcast or recording of an actual
video conferencing call. video conferencing call.
The length of the chosen video sequence is a tradeoff. If it is too The length of the chosen video sequence is a tradeoff. If it is too
long, it will be difficult to manage the data structures containing long, it will be difficult to manage the data structures containing
the traces. If it is too short, there will be an obvious periodic the traces. If it is too short, there will be an obvious periodic
pattern in the output frame sizes, leading to biased results when pattern in the output frame sizes, leading to biased results when
evaluating congestion control performance. It has been empirically evaluating congestion control performance. It has been empirically
determined that a sequence with a length between 2 and 4 minutes determined that a sequence 2 to 4 minutes in length sufficiently
strikes a fair tradeoff. avoids the periodic pattern.
Given the chosen raw video sequence, denoted S, one can use a live Given the chosen raw video sequence, denoted S, one can use a live
encoder, e.g. some implementation of [H264] or [HEVC], to produce a encoder, e.g. some implementation of [H264] or [HEVC], to produce a
set of encoded sequences. As discussed in Section 3, the output set of encoded sequences. As discussed in Section 3, the output
bitrate of the live encoder can be achieved by tuning three input bitrate of the live encoder can be achieved by tuning three input
parameters: quantization step size, frame rate, and picture parameters: quantization step size, frame rate, and picture
resolution. In order to simplify the choice of these parameters for resolution. In order to simplify the choice of these parameters for
a given target rate, one can typically assume a fixed frame rate a given target rate, one can typically assume a fixed frame rate
(e.g. 30 fps) and a fixed resolution (e.g., 720p) when configuring (e.g. 30 fps) and a fixed resolution (e.g., 720p) when configuring
the live encoder. See Section 6.3 for a discussion on how to relax the live encoder. See Section 6.3 for a discussion on how to relax
these assumptions. these assumptions.
Following these simplifications, the chosen encoder can be configured Following these simplifications, the chosen encoder can be configured
to start at a constant target bitrate, then vary the quantization to start at a constant target bitrate, then vary the quantization
step size (internally via the video encoder rate controller) to meet step size (internally via the video encoder rate controller) to meet
various externally specified target rates. It can be further assumed various externally specified target rates. It can be further assumed
the first frame is encoded as an I-frame and the rest are P-frames. the first frame is encoded as an I-frame and the rest are P-frames
For live encoding, the encoder rate control algorithm typically does (see, e.g., [H264] for definitions of I- and P-frames). For live
not use knowledge of frames in the future when encoding a given encoding, the encoder rate control algorithm typically does not use
frame. knowledge of frames in the future when encoding a given frame.
Given the minimum and maximum bitrates at which the synthetic codec Given the minimum and maximum bitrates at which the synthetic codec
is to operate (denoted as R_min and R_max, see Section 4), the entire is to operate (denoted as R_min and R_max, see Section 4), the entire
range of target bitrates can be divided into n_s + 1 bitrate steps of range of target bitrates can be divided into n_s steps. This leads
length l = (R_max - R_min) / n_s. The following simple algorithm is to a encoding bitrate ladder of (n_s + 1) choices equally spaced
used to encode the raw video sequence. apart by the step length l = (R_max - R_min)/n_s. The following
simple algorithm is used to encode the raw video sequence.
r = R_min r = R_min
while r <= R_max do while r <= R_max do
Traces[r] = encode_sequence(S, r, e) Traces[r] = encode_sequence(S, r, e)
r = r + l r = r + l
The function encode_sequence takes as input parameters, respectively, The function encode_sequence takes as input parameters, respectively,
a raw video sequence (S), a constant target rate (r), and an encoder a raw video sequence (S), a constant target rate (r), and an encoder
rate control algorithm (e); it returns a vector with the sizes of rate control algorithm (e); it returns a vector with the sizes of
frames in the order they were encoded. The output vector is stored frames in the order they were encoded. The output vector is stored
in a map structure called Traces, whose keys are bitrates and whose in a map structure called Traces, whose keys are bitrates and whose
values are vectors of frame sizes. values are vectors of frame sizes.
The choice of a value for n_s is important, as it determines the The choice of a value for the number of bitrate steps n_s is
number of vectors of frame sizes stored in the map Traces. The important, since it determines the number of vectors of frame sizes
minimum value one can choose for n_s is 1, and the maximum value stored in the map Traces. The minimum value one can choose for n_s
depends on the amount of memory available for holding the map Traces. is 1; the maximum value depends on the amount of memory available for
A reasonable value for n_s is one that results in steps of length l = holding the map Traces. A reasonable value for n_s is one that
200 kbps. The next section will discuss further the choice of the results in steps of length l = 200 kbps. The next section will
step length l. discuss further the choice of step length l.
Finally, note that, as mentioned in previous sections, R_min and Finally, note that, as mentioned in previous sections, R_min and
R_max may be modified after the initial sequences are encoded. R_max may be modified after the initial sequences are encoded.
Hence, the algorithm described in the next section also covers the Henceforth, for notational clarity, we refer to the bitrate range of
cases when the current target bitrate is less than R_min, or greater the trace file as [Rf_min, Rf_max]. The algorithm described in the
than R_max. next section also covers the cases when the current target bitrate is
less than Rf_min, or greater than Rf_max.
6.2. Using the traces in the synthetic codec 6.2. Using the traces in the synthetic codec
The main idea behind the trace-driven synthetic codec is that it The main idea behind the trace-driven synthetic codec is that it
mimics the rate adaptation behavior of a real live codec upon dynamic mimics the rate adaptation behavior of a real live codec upon dynamic
updates of the target rate R_v by the congestion control module. It updates of the target bitrate request R_v by the congestion control
does so by switching to a different frame size vector stored in the module. It does so by switching to a different frame size vector
map Traces when needed. stored in the map Traces when needed.
6.2.1. Main algorithm 6.2.1. Main algorithm
The main algorithm for rate adaptation in the synthetic codec The main algorithm for rate adaptation in the synthetic codec
maintains two variables: r_current and t_current. maintains two variables: r_current and t_current.
o The variable r_current points to one of the keys of map Traces. o The variable r_current points to one of the keys of map Traces.
Upon a change in the value of R_v, typically because the Upon a change in the value of R_v, typically because the
congestion controller detects that the network conditions have congestion controller detects that the network conditions have
changed, r_current is updated to the greatest key in Traces that changed, r_current is updated based on R_v as follows:
is less than or equal to the new value of R_v. It is assumed that
the value of R_v is clipped within the range [R_min, R_max].
r_current = r R_ref = min (Rf_max, max(Rf_min, R_v))
such that
(r in keys(Traces) and r_current = r
r <= R_v and such that
(not(exists) r' in keys(Traces) such that r <r'<= R_v)) (r in keys(Traces) and
r <= R_ref and
(not(exists) r' in keys(Traces) such that r <r'<= R_ref))
o The variable t_current is an index to the frame size vector stored o The variable t_current is an index to the frame size vector stored
in Traces[r_current]. It is updated every time a new frame is in Traces[r_current]. It is updated every time a new frame is
due. It is assumed that all vectors stored Traces to have the due. It is assumed that all vectors stored in Traces have the
same size, denoted as size_traces. The following equation governs same size, denoted as size_traces. The following equation governs
the update of t_current: the update of t_current:
if t_current < SkipFrames then if t_current < SkipFrames then
t_current = t_current + 1 t_current = t_current + 1
else else
t_current = ( (t_current + 1 - SkipFrames) t_current = ((t_current + 1 - SkipFrames)
% (size_traces-SkipFrames)) % (size_traces-SkipFrames)) + SkipFrames
+ SkipFrames
where operator % denotes modulo, and SkipFrames is a predefined where operator % denotes modulo, and SkipFrames is a predefined
constant that denotes the number of frames to be skipped at the constant that denotes the number of frames to be skipped at the
beginning of frame size vectors after t_current has wrapped around. beginning of frame size vectors after t_current has wrapped around.
The point of constant SkipFrames is avoiding the effect of The point of constant SkipFrames is avoiding the effect of
periodically sending a large I-frame followed by several smaller- periodically sending a large I-frame followed by several smaller-
than-average P-frames. A typical value of SkipFrames is 20, although than-average P-frames. A typical value of SkipFrames is 20, although
it could be set to 0 if one is interested in studying the effect of it could be set to 0 if one is interested in studying the effect of
sending I-frames periodically. sending I-frames periodically.
The initial value of r_current is set to R_min, and the initial value The initial value of r_current is set to R_min, and the initial value
of t_current set to 0. of t_current is set to 0.
When a new frame is due, its size can be calculated following one of When a new frame is due, its size can be calculated following one of
the three cases below: the three cases below:
a) R_min <= R_v < Rmax: the output frame size is calculated via a) Rf_min <= R_v < Rf_max: the output frame size is calculated via
linear interpolation of the frame sizes appearing in linear interpolation of the frame sizes appearing in
Traces[r_current] and Traces[r_current + l]. The interpolation is Traces[r_current] and Traces[r_current + l]. The interpolation is
done as follows: done as follows:
size_lo = Traces[r_current][t_current] size_lo = Traces[r_current][t_current]
size_hi = Traces[r_current + l][t_current] size_hi = Traces[r_current + l][t_current]
distance_lo = (R_v - r_current) / l distance_lo = (R_v - r_current) / l
framesize = size_hi*distance_lo + size_lo*(1-distance_lo) framesize = size_hi*distance_lo + size_lo*(1-distance_lo)
b) R_v < R_min: the output frame size is calculated via scaling with b) R_v < Rf_min: the output frame size is calculated via scaling
respect to the lowest bitrate R_min, as follows: with respect to the lowest bitrate Rf_min in the trace file, as
follows:
factor = R_v / R_min w = R_v / Rf_min
framesize = max(1, factor * Traces[R_min][t_current]) framesize = max(fs_min, factor * Traces[Rf_min][t_current])
c) R_v >= R_max: the output frame size is calculated by scaling with c) R_v >= Rf_max: the output frame size is calculated by scaling
respect to the highest bitrate R_max: with respect to the highest bitrate Rf_max in the trace file, as
follows:
factor = R_v / R_max w = R_v / Rf_max
framesize = factor * Traces[R_max][t_current] framesize = min(fs_max, w * Traces[Rf_max][t_current])
In case b), the minimum output size is set to 1 byte, since the value In cases b) and c), floating-point arithmetic is used for computing
of factor can be arbitrarily close to 0. the scaling factor w. The resulting value of the instantaneous frame
size (framesize) is further clipped within a reasonable range between
fs_min (e.g., 10 bytes) and fs_max (e.g., 1MB).
6.2.2. Notes to the main algorithm 6.2.2. Notes to the main algorithm
Note that main algorithm as described above can be further extended Note that the main algorithm as described above can be further
to mimic some additional typical behaviors of a live video encoder. extended to mimic some additional typical behaviors of a live video
Two examples are given below: encoder. Two examples are given below:
o I-frames on demand: The synthetic codec can be extended to o I-frames on demand: The synthetic codec can be extended to
simulate the sending of I-frames on demand, e.g., as a reaction to simulate the sending of I-frames on demand, e.g., as a reaction to
losses. To implement this extension, the codec's incoming losses. To implement this extension, the codec's incoming
interface (see (a) in Figure 1) is augmented with a new function interface (see (a) in Figure 1) is augmented with a new function
to request a new I-frame. Upon calling such function, t_current to request a new I-frame. Upon calling such function, t_current
is reset to 0. is reset to 0.
o Variable step length l between R_min and R_max: In the main o Variable step length l between R_min and R_max: In the main
algorithm, the step length l is fixed for ease of explanation. algorithm, the step length l is fixed for ease of explanation.
skipping to change at page 13, line 44 skipping to change at page 14, line 8
variable step length. The rationale behind this modification is variable step length. The rationale behind this modification is
that the difference between 400 kbps and 600 kbps as target that the difference between 400 kbps and 600 kbps as target
bitrate is much more significant than the difference between 4400 bitrate is much more significant than the difference between 4400
kbps and 4600 kbps. For example, one could define steps of length kbps and 4600 kbps. For example, one could define steps of length
200 Kbps under 1 Mbps, then steps of length 300 Kbps between 1 200 Kbps under 1 Mbps, then steps of length 300 Kbps between 1
Mbps and 2 Mbps; 400 Kbps between 2 Mbps and 3 Mbps, and so on. Mbps and 2 Mbps; 400 Kbps between 2 Mbps and 3 Mbps, and so on.
6.3. Varying frame rate and resolution 6.3. Varying frame rate and resolution
The trace-driven synthetic codec model explained in this section is The trace-driven synthetic codec model explained in this section is
relatively simple due to fixed frame rate and frame resolution. The relatively simple due to the choice of fixed frame rate and frame
model can extended further to accommodate variable frame rate and/or resolution. The model can be extended further to accommodate
variable spatial resolution. variable frame rate and/or variable spatial resolution.
When the encoded picture quality at a given bitrate is low, one can When the encoded picture quality at a given bitrate is low, one can
potentially decrease either the frame rate (if the video sequence is potentially decrease either the frame rate (if the video sequence is
currently in low motion) or the spatial resolution in order to currently in low motion) or the spatial resolution in order to
improve quality-of-experince (QoE) in the overall encoded video. On improve quality-of-experience (QoE) in the overall encoded video. On
the other hand, if target bitrate increases to a point where there is the other hand, if target bitrate increases to a point where there is
no longer a perceptible improvement in the picture quality of no longer a perceptible improvement in the picture quality of
individual frames, then one might afford to increase the spatial individual frames, then one might afford to increase the spatial
resolution or the frame rate (useful if the video is currently in resolution or the frame rate (useful if the video is currently in
high motion). high motion).
Many techniques have been proposed to choose over time the best Many techniques have been proposed to choose over time the best
combination of encoder quatization step size, frame rate, and spatial combination of encoder quantization step size, frame rate, and
resolution in order to maximize the quality of live video codecs spatial resolution in order to maximize the quality of live video
[Ozer2011][Hu2010]. Future work may consider extending the trace- codecs [Ozer2011][Hu2010]. Future work may consider extending the
driven codec to accommodate variable frame rate and/or resolution. trace-driven codec to accommodate variable frame rate and/or
resolution.
From the perspective of congestion control, varying the spatial From the perspective of congestion control, varying the spatial
resolution typically requires a new intra-coded frame to be resolution typically requires a new intra-coded frame to be
generated, thereby incurring a temporary burst in the output traffic generated, thereby incurring a temporary burst in the output traffic
pattern. The impact of frame rate change tends to be more subtle: pattern. The impact of frame rate change tends to be more subtle:
reducing frame rate from high to low leads to sparsely spaced larger reducing frame rate from high to low leads to sparsely spaced larger
encoded packets instead of many densely spaced smaller packets. Such encoded packets instead of many densely spaced smaller packets. Such
difference in traffic profiles may still affect the performance of difference in traffic profiles may still affect the performance of
congestion control, especially when outgoing packets are not paced by congestion control, especially when outgoing packets are not paced by
the media transport module. Investigation of varying frame rate and the media transport module. Investigation of varying frame rate and
resolution are left for future work. resolution are left for future work.
7. Combining The Two Models 7. Combining The Two Models
It is worthwhile noting that the statistical and trace-driven models It is worthwhile noting that the statistical and trace-driven models
each has its own advantages and drawbacks. Both models are fairly each have their own advantages and drawbacks. Both models are fairly
simple to implement. It takes significantly greater effort to fit simple to implement. It takes significantly greater effort to fit
the parameters of a statistical model to actual encoder output data the parameters of a statistical model to actual encoder output data.
whereas it is straightforward for a trace-driven model to obtain In contrast, it is straightforward for a trace-driven model to obtain
encoded frame size data. On the other hand, once validated, the encoded frame size data. Once validated, the statistical model is
statistical model is more flexible in mimicking a wide range of more flexible in mimicking a wide range of encoder/content behaviors
encoder/content behaviors by simply varying the correponding by simply varying the corresponding parameters in the model. In this
parameters in the model. In this regard, a trace-driven model relies regard, a trace-driven model relies -- by definition -- on additional
-- by definition -- on additional data collection efforts for data collection efforts for accommodating new codecs or video
accommodating new codecs or video contents. contents.
In general, the trace-driven model is more realistic for mimicking In general, the trace-driven model is more realistic for mimicking
ongoing, steady-state behavior of a video traffic source whereas the the ongoing, steady-state behavior of a video traffic source with
statistical model is more versatile for simulating its transient- fluctuations around a constant target rate. In contrast, the
state behavior such as a sudden rate change. It is also possible to statistical model is more versatile for simulating the behavior of a
combine both methods into a hybrid model, so that the steady-state video stream in transient, such as when encountering sudden rate
behavior is driven by traces during steady-state and the transient- changes. It is also possible to combine both methods into a hybrid
state behavior is driven by the statistical model. model. In this case, the steady-state behavior is driven by traces
during steady state and the transient-state behavior is driven by the
statistical model.
transient +---------------+ transient +---------------+
state | Generate next | state | Generate next |
+------>| K_d transient | +------>| K_d transient |
+-------------+ / | frames | +-----------------+ / | frames |
R_v | Compare | / +---------------+ R_v | Compare against | / +---------------+
------->| against |/ ------>| previous |/
| previous | | target bitrate |\
| target rate |\ +-----------------+ \ +---------------+
+-------------+ \ +---------------+ \ | Generate next |
\ | Generate next | +------>| frame from |
+------>| frame from | steady | trace |
steady | trace | state +---------------+
state +---------------+
Figure 3: A hybrid video traffic model Figure 3: A hybrid video traffic model
As shown in Figure 3, the video traffic model operates in transient As shown in Figure 3, the video traffic model operates in a transient
state if the requested target rate R_v is substantially higher than state if the requested target rate R_v is substantially different
the previous target, or else it operates in steady state. During the from the previous target, or else it operates in steady state.
transient state, a total of K_d frames are generated by the During the transient state, a total of K_d frames are generated by
statistical model, resulting in one (1) big burst frame with size K_B the statistical model, resulting in one (1) big burst frame with size
followed by K_d-1 smaller frames. When operating at steady-state, K_B followed by K_d-1 smaller frames. When operating at steady
the video traffic model simply generates a frame according to the state, the video traffic model simply generates a frame according to
trace-driven model given the target rate, while modulating the frame the trace-driven model given the target rate, while modulating the
interval according to the distribution specified by the statistical frame interval according to the distribution specified by the
model. One example criterion for determining whether the traffic statistical model. One example criterion for determining whether the
model should operate in transient state is whether the rate increase traffic model should operate in a transient state is whether the rate
exceeds 10% of previous target rate. Finally, as this model follows change exceeds 10% of the previous target rate. Finally, as this
transient state behavior dictated by the statistical model, upon a model follows transient-state behavior dictated by the statistical
substantial rate change, the model will follow the time-damping model, upon a substantial rate change, the model will follow the
mechanism defined in Section 5.1, which is governed by parameter time-damping mechanism as defined in Section 5.1, which is governed
tau_v. by parameter tau_v.
8. Implementation Status 8. Implementation Status
The statistical model has been implemented as a traffic generator The statistical, trace-driven, and hybrid models as described in this
module within the [ns-2] network simulation platform. draft have been implemented as a stand-alone, platform-independent
synthetic traffic source module. It can be easily integrated into
More recently, the statistical, trace-driven, and hybrid models have network simulation platforms such as [ns-2] and [ns-3], as well as
been implemented as a stand-alone, platform-independent traffic testbeds using a real network. The stand-alone traffic source module
source module. This can be easily integrated into network simulation is available as an open source implementation at [Syncodecs].
platforms such as [ns-2] and [ns-3], as well as testbeds using a real
network. The stand-alone traffic source module is available as an
open source implementation at [Syncodecs].
9. IANA Considerations 9. IANA Considerations
There are no IANA impacts in this memo. There are no IANA impacts in this memo.
10. Security Considerations 10. Security Considerations
It is important to evaluate RTP-based congestion control schemes The synthetic video traffic models as described in this draft do not
using realistic traffic patterns, so as to ensure stable operations impose any security threats. They are designed to mimic realistic
of the network. Therefore, it is RECOMMENDED that candidate RTP- traffic patterns for evaluating candidate RTP-based congestion
based congestion control algorithms be tested using the video traffic control algorithms, so as to ensure stable operations of the network.
models presented in this draft before wide deployment over the It is RECOMMENDED that candidate algorithms be tested using the video
Internet. traffic models presented in this draft before wide deployment over
the Internet. If the generated synthetic traffic flows are sent over
the Internet, they also need to be congestion controlled.
11. References 11. References
11.1. Normative References 11.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997, DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>. <https://www.rfc-editor.org/info/rfc2119>.
skipping to change at page 17, line 14 skipping to change at page 17, line 27
[ns-2] "The Network Simulator - ns-2", [ns-2] "The Network Simulator - ns-2",
<http://www.isi.edu/nsnam/ns/>. <http://www.isi.edu/nsnam/ns/>.
[ns-3] "The Network Simulator - ns-3", <https://www.nsnam.org/>. [ns-3] "The Network Simulator - ns-3", <https://www.nsnam.org/>.
[Ozer2011] [Ozer2011]
Ozer, J., "Video Compression for Flash, Apple Devices and Ozer, J., "Video Compression for Flash, Apple Devices and
HTML5", ISBN 13:978-0976259503, 2011. HTML5", ISBN 13:978-0976259503, 2011.
[Papoulis]
Papoulis, A., "Probability, Random Variables and
Stochastic Processes", 2002.
[RFC5104] Wenger, S., Chandra, U., Westerlund, M., and B. Burman,
"Codec Control Messages in the RTP Audio-Visual Profile
with Feedback (AVPF)", RFC 5104, DOI 10.17487/RFC5104,
February 2008, <https://www.rfc-editor.org/info/rfc5104>.
[Syncodecs] [Syncodecs]
Mena, S., D'Aronco, S., and X. Zhu, "Syncodecs: Synthetic Mena, S., D'Aronco, S., and X. Zhu, "Syncodecs: Synthetic
codecs for evaluation of RMCAT work", codecs for evaluation of RMCAT work",
<https://github.com/cisco/syncodecs>. <https://github.com/cisco/syncodecs>.
[Tanwir2013] [Tanwir2013]
Tanwir, S. and H. Perros, "A Survey of VBR Video Traffic Tanwir, S. and H. Perros, "A Survey of VBR Video Traffic
Models", IEEE Communications Surveys and Tutorials, vol. Models", IEEE Communications Surveys and Tutorials, vol.
15, no. 5, pp. 1778-1802., October 2013. 15, no. 5, pp. 1778-1802., October 2013.
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