draft-ietf-rmcat-video-traffic-model-00.txt   draft-ietf-rmcat-video-traffic-model-01.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: July 18, 2016 Z. Sarker Expires: January 9, 2017 Z. Sarker
Ericsson AB Ericsson AB
January 15, 2016 July 8, 2016
Modeling Video Traffic Sources for RMCAT Evaluations Modeling Video Traffic Sources for RMCAT Evaluations
draft-ietf-rmcat-video-traffic-model-00 draft-ietf-rmcat-video-traffic-model-01
Abstract Abstract
This document describes two reference video traffic source models for This document describes two reference video traffic source models for
evaluating RMCAT candidate algorithms. The first model statistically evaluating RMCAT candidate algorithms. The first model statistically
characterizes the behavior of a live video encoder in response to characterizes the behavior of a live video encoder in response to
changing requests on target video rate. The second model is trace- changing requests on target video rate. The second model is trace-
driven, and emulates the encoder output by scaling the pre-encoded driven, and emulates the encoder output by scaling the pre-encoded
video frame sizes from a widely used video test sequence. Both video frame sizes from a widely used video test sequence. Both
models are designed to strike a balance between simplicity, models are designed to strike a balance between simplicity,
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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-
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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 July 18, 2016. This Internet-Draft will expire on January 9, 2017.
Copyright Notice Copyright Notice
Copyright (c) 2016 IETF Trust and the persons identified as the Copyright (c) 2016 IETF Trust and the persons identified as the
document authors. All rights reserved. document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents Provisions Relating to IETF Documents
(http://trustee.ietf.org/license-info) in effect on the date of (http://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents publication of this document. Please review these documents
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5.1. Time-damped response to target rate update . . . . . . . 7 5.1. Time-damped response to target rate update . . . . . . . 7
5.2. Temporary burst/oscillation during transient . . . . . . 7 5.2. Temporary burst/oscillation during transient . . . . . . 7
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 . . . . . . . . 8 5.4. Rate range limit imposed by video content . . . . . . . . 8
6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 8 6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 8
6.1. Choosing the video sequence and generating the traces . . 9 6.1. Choosing the video sequence and generating the traces . . 9
6.2. Using the traces in the syntethic codec . . . . . . . . . 10 6.2. Using the traces in the syntethic codec . . . . . . . . . 10
6.2.1. Main algorithm . . . . . . . . . . . . . . . . . . . 10 6.2.1. Main algorithm . . . . . . . . . . . . . . . . . . . 10
6.2.2. Notes to the main algorithm . . . . . . . . . . . . . 12 6.2.2. Notes to the main algorithm . . . . . . . . . . . . . 12
6.3. Varying frame rate and resolution . . . . . . . . . . . . 12 6.3. Varying frame rate and resolution . . . . . . . . . . . . 12
7. Comparing and Combining The Two Models . . . . . . . . . . . 13 7. Combining The Two Models . . . . . . . . . . . . . . . . . . 13
8. Implementation Status . . . . . . . . . . . . . . . . . . . . 14 8. Implementation Status . . . . . . . . . . . . . . . . . . . . 14
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14 9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 14 10. References . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.1. Normative References . . . . . . . . . . . . . . . . . . 14 10.1. Normative References . . . . . . . . . . . . . . . . . . 15
10.2. Informative References . . . . . . . . . . . . . . . . . 14 10.2. Informative References . . . . . . . . . . . . . . . . . 15
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 15 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 15
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.
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 RMCAT algorithm On the other hand, evaluation results of a candidate RMCAT algorithm
should mostly reflect performance of the congestion control module, should mostly reflect performance of the congestion control module,
and somewhat decouple from pecularities of any specific video codec. and somewhat decouple from peculiarities of any specific video codec.
It is also desirable that evaluation tests are repeatable, and be It is also desirable that evaluation tests are repeatable, and be
easily duplicated across different candidate algorithms. easily duplicated across different candidate 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 RMCAT algorithms using a synthetic video traffic source evaluate RMCAT algorithms using a synthetic video traffic source
model that captures key characteristics of the behavior of a live model that captures key characteristics of the behavior of a live
video encoder. To this end, this draft presents two reference video encoder. To this end, this draft presents two reference
models. The first is based on statistical modelling; the second is models. The first is based on statistical modelling; the second is
trace-driven. The draft also discusses the pros and cons of each trace-driven. The draft also discusses the pros and cons of each
approach, as well as the possibility to combine both. approach, as well as the how both approaches can be combined.
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", "MAY", and "OPTIONAL" in this "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described RFC2119 [RFC2119]. document are to be interpreted as described RFC2119 [RFC2119].
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
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o To change bitrate. This includes ability to change framerate and/ o To change bitrate. This includes ability to change framerate and/
or spatial resolution, or to skip frames when required. or spatial resolution, or to skip frames when required.
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 delay in convergence to the target bitrate. o To 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 exists many different approaches in developing a While there exist many different approaches in developing a synthetic
synthetic video traffic model, it is desirable that the outcome video traffic model, it is desirable that the outcome follows a few
follows a few common characteristics, as outlined below. common characteristics, as outlined below.
o Low computational complexity: The model should be computationally o Low computational complexity: The model should be computationally
lightweight, otherwise it defeats the whole purpose of serving as lightweight, otherwise it defeats the whole purpose of serving as
a substitute for a live video encoder. a substitute for a live video encoder.
o Temporal pattern similarity: The individual traffic trace o Temporal pattern similarity: The individual traffic trace
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 should match the o Statistical resemblance: The synthetic traffic should match the
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The synthetic video encoder takes in raw video frames captured by the The synthetic video encoder takes in raw video frames captured by the
camera and then dynamically generates a sequence of encoded video camera and then dynamically generates a sequence of encoded video
frames with varying size and interval. These encoded frames are frames with varying size and interval. These encoded frames are
processed by other modules in order to transmit the video stream over processed by other modules in order to transmit the video stream over
the network. During the lifetime of a video transmission session, the network. During the lifetime of a video transmission session,
the synthetic video encoder will typically be required to adapt its the synthetic video encoder 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 our model, the synthetic video encoder module has group of In our model, the synthetic video encoder 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 encoder may accept. The list is not exhaustive and can be video encoder may accept. The list is not exhaustive and can be
complemented by other interface calls if deemed necessary. complemented by other interface calls if deemed necessary.
o Target rate R_v(t): requested at time t, typically from the o Target rate R_v(t): requested at time t, typically from the
congestion control module. Depending on the congestion control congestion control module. Depending on the congestion control
algorithm in use, the update requests can either be periodic algorithm in use, the update requests can either be periodic
(e.g., once per second), or on-demand (e.g., only when a drastic (e.g., once per second), or on-demand (e.g., only when a drastic
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-------------------+ +--------------------> -------------------+ +-------------------->
interface from interface to interface from interface to
other modules (a) other modules (b) other modules (a) other modules (b)
Figure 1: Interaction between synthetic video encoder and other Figure 1: Interaction between synthetic video encoder and other
modules at the sender modules at the sender
5. A Statistical Reference Model 5. A Statistical Reference Model
In this section, we describe one simple statistical model of the live In this section, we describe one simple statistical model of the live
video encoder traffic source. Figure 2 summarizes the list of tuable video encoder traffic source. Figure 2 summarizes the list of
parameters in this statistical model. A more comprehensive survey of tunable parameters in this statistical model. A more comprehensive
popular methods for modelling video traffic source behavior can be survey of popular methods for modelling video traffic source behavior
found in [Tanwir2013]. can be found in [Tanwir2013].
+---------------+--------------------------------+----------------+ +---------------+--------------------------------+----------------+
| Notation | Parameter Name | Example Value | | Notation | Parameter Name | Example Value |
+--------------+---------------------------------+----------------+ +--------------+---------------------------------+----------------+
| R_v(t) | Target rate request at time t | 1 Mbps | | R_v(t) | Target rate request at time t | 1 Mbps |
| R_o(t) | Output rate at time t | 1.2 Mbps | | R_o(t) | Output rate at time t | 1.2 Mbps |
| tau_v | Encoder reaction latency | 0.2 s | | tau_v | Encoder reaction latency | 0.2 s |
| K_d | Burst duration during transient | 5 frames | | K_d | Burst duration during transient | 5 frames |
| K_r | Burst size during transient | 5:1 | | K_r | Burst size during transient | 5:1 |
| R_e(t) | Error in output rate at time t | 0.2 Mbps | | R_e(t) | Error in output rate at time t | 0.2 Mbps |
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occasional burst of large frames are followed by smaller-than average occasional burst of large frames are followed by smaller-than average
encoded frames. encoded frames.
This temporary burst is characterized by two parameters: This temporary burst is characterized by two parameters:
o burst duration K_d: number frames in the burst event; and o burst duration K_d: number frames in the burst event; and
o burst size K_r: ratio of a burst frame and average frame size at o burst size K_r: ratio of a burst frame and average frame size at
steady state. steady 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 insersion 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_r are fitted to reflect the packet losses. The values of K_d and K_r are fitted to reflect the
typical ratio between I and P frames for a given video content. typical ratio between I and P frames for a given video content.
5.3. Output rate fluctuation at steady state 5.3. Output rate fluctuation at steady state
We model output rate R_o as randomly fluctuating around the target We model output rate R_o as randomly fluctuating around the target
rate R_v after convergence. There are two variants in modeling the rate R_v after convergence. There are two variants in modeling the
random fluctuation R_e = R_o - R_v: random fluctuation R_e = R_o - R_v:
o As normal distribution: with a mean of zero and a standard o As normal distribution: with a mean of zero and a standard
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6.1. Choosing the video sequence and generating the traces 6.1. Choosing the video sequence and generating the traces
The first step we need to perform is a careful choice of a set of The first step we need to perform is a careful choice of a set of
video sequences that are representative of the use cases we want to video sequences that are representative of the use cases we want to
model. Our use case here is video conferencing, so we must choose a model. Our use case here is video conferencing, so we must choose a
low-motion sequence that resembles a "talking head", for instance a low-motion sequence that resembles a "talking head", for instance a
news broadcast or a video capture of an actual conference call. news broadcast or a video capture of an actual conference 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 we will produce in the next steps. If it is too short, the traces. If it is too short, there will be an obvious periodic
there will be an obvious periodic pattern in the output frame sizes, pattern in the output frame sizes, leading to biased results when
leading to biased results when evaluating congestion controller evaluating congestion controller performance. In our experience, a
performance. In our experience, a one-minute-long sequence is a fair one-minute-long sequence is a fair tradeoff.
tradeoff.
Once we have chosen the raw video sequence, denoted S, we use a live Once we have chosen the raw video sequence, denoted S, we use a live
encoder, e.g. [H264] or [HEVC] to produce a set of encoded encoder, e.g. [H264] or [HEVC] to produce a set of encoded
sequences. As discussed in Section 3, a live encoder's output sequences. As discussed in Section 3, a live encoder's output
bitrate can be tuned by varying three input parameters, namely, bitrate can be tuned by varying three input parameters, namely,
quantization step size, frame rate, and picture resolution. In order quantization step size, frame rate, and picture resolution. In order
to simplify the choice of these parameters for a given target rate, to simplify the choice of these parameters for a given target rate,
we assume a fixed frame rate (e.g. 25 fps) and a fixed resolution we assume a fixed frame rate (e.g. 25 fps) and a fixed resolution
(e.g., 480p). See section 6.3 for a discussion on how to relax these (e.g., 480p). See section 6.3 for a discussion on how to relax these
assumptions. assumptions.
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The choice of a value for n_s is important, as it determines the The choice of a value for n_s is important, as it determines the
number of frame size vectors stored in map Traces. The minimum value number of frame size vectors stored in map Traces. The minimum value
one can choose for n_s is 1, and its maximum value depends on the one can choose for n_s is 1, and its maximum value depends on the
amount of memory available for holding the map Traces. A reasonable amount of memory available for holding the map Traces. A reasonable
value for n_s is one that makes the steps' length l = 200 kbps. We value for n_s is one that makes the steps' length l = 200 kbps. We
will further discuss step length l in the next section. will further discuss step length l in the next section.
6.2. Using the traces in the syntethic codec 6.2. Using the traces in the syntethic codec
The main idea behind the trace-based synthetic codec is that it The main idea behind the trace-driven synthetic codec is that it
mimics a real live codec's rate adaptation when the congestion mimics a real live codec's rate adaptation when the congestion
controller updates the target rate R_v(t). It does so by switching controller updates the target rate R_v(t). It does so by switching
to a different frame size vector stored in the map Traces when to a different frame size vector stored in the map Traces when
needed. needed.
6.2.1. Main algorithm 6.2.1. Main algorithm
We maintain two variables r_current and t_current: We maintain two variables r_current and t_current:
* r_current points to one of the keys of the map Traces. Upon a * r_current points to one of the keys of the map Traces. Upon a
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factor = R_v(t) / R_max factor = R_v(t) / R_max
framesize = factor * Traces[R_max][t_current] framesize = factor * Traces[R_max][t_current]
In case b), we set the minimum to 1 byte, since the value of factor In case b), we set the minimum to 1 byte, since the value of factor
can be arbitrarily close to 0. can be arbitrarily close to 0.
6.2.2. Notes to the main algorithm 6.2.2. Notes to the main algorithm
* Reacting to changes in target bitrate. Similarly to the * Reacting to changes in target bitrate. Similarly to the
statistical model presented in Section 5, the trace-based synthetic statistical model presented in Section 5, the trace-driven synthetic
codec can have a time bound, tau_v, to reacting to target bitrate codec can have a time bound, tau_v, to reacting to target bitrate
changes. If the codec has reacted to an update in R_v(t) at time t, changes. If the codec has reacted to an update in R_v(t) at time t,
it will delay any further update to R_v(t) to time t + tau_v. Note it will delay any further update to R_v(t) to time t + tau_v. Note
that, in any case, the value of tau_v cannot be chosen shorter than that, in any case, the value of tau_v cannot be chosen shorter than
the time between frames, i.e. the inverse of the frame rate. the time between frames, i.e. the inverse of the frame rate.
* I-frames on demand. The synthetic codec could be extended to * I-frames on demand. The synthetic codec could 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 API is augmented losses. To implement this extension, the codec's API is augmented
with a new function to request a new I-frame. Upon calling such with a new function to request a new I-frame. Upon calling such
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if the range [R_min, R_max] is very wide, it is also possible to if the range [R_min, R_max] is very wide, it is also possible to
define a set of steps with a non-constant length. The idea behind define a set of steps with a non-constant length. The idea behind
this modification is that the difference between 400 kbps and 600 this modification is that the difference between 400 kbps and 600
kbps as bitrate is much more important than the difference between kbps as bitrate is much more important than the difference between
4400 kbps and 4600 kbps. For example, one could define steps of 4400 kbps and 4600 kbps. For example, one could define steps of
length 200 Kbps under 1 Mbps, then length 300 kbps between 1 Mbps and length 200 Kbps under 1 Mbps, then length 300 kbps between 1 Mbps and
2 Mbps, 400 kbps between 2 Mbps and 3 Mbps, and so on. 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-based synthetic codec model explained in this section is The trace-driven synthetic codec model explained in this section is
relatively simple because we have fixed the frame rate and the frame relatively simple because we have fixed the frame rate and the frame
resolution. The model could be extended to have variable frame rate, resolution. The model could be extended to have variable frame rate,
variable spatial resolution, or both. variable spatial resolution, or both.
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 the frame rate (if the video sequence is potentially decrease 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-experince (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 quatization step size, frame rate, and spatial
resolution in order to maximize the quality of live video codecs resolution in order to maximize the quality of live video codecs
[Ozer2011][Hu2010]. Future work may consider extending the trace- [Ozer2011][Hu2010]. Future work may consider extending the trace-
based codec to accommodate variable frame rate and/or resolution. 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 at congestion control, especially when outgoing packets are not paced at
the transport module. We leave the investigation of varying frame the transport module. We leave the investigation of varying frame
rate to future work. rate to future work.
7. Comparing and Combining The Two Models 7. Combining The Two Models
It is worthwhile noting that the statistical and trace-based models It is worthwhile noting that the statistical and trace-driven models
each has its own advantages and drawbacks. Both models are fairly each has its own advantages and drawbacks. While both models are
simple to implement. However, it takes significantly more effort to fairly simple to implement, it takes significantly greater effort to
fit the parameters of a statistical model to actual encoder output fit the parameters of a statistical model to actual encoder output
data whereas a trace-based model does not require such fitting. On data whereas it is straightforward for a trace-driven model to obtain
the other hand, once validated, the statistical model is more encoded frame size data. On the other hand, once validated, the
flexible in mimicking a wide range of encoder/content behavior by statistical model is more flexible in mimicking a wide range of
simply varying the correponding parameters in the model. In encoder/content behaviors by simply varying the correponding
contrast, a trace-driven model relies, by definition, on additional parameters in the model. In this regard, a trace-driven model relies
data collection efforts for accommodating new codecs or video -- by definition -- on additional data collection efforts for
contents. accommodating new codecs or video contents.
In general, trace-based model is more realistic for mimicking In general, trace-driven model is more realistic for mimicking
ongoing, steady-state behavior of a video traffic source whereas ongoing, steady-state behavior of a video traffic source whereas
statistical model is more versatile for simulating transient events statistical model is more versatile for simulating transient events
(e.g., when target rate changes from A to B with temporary bursts (e.g., when target rate changes from A to B with temporary bursts
during the transition). Therefore, it may be desirable to combine during the transition). It is also possible to combine both models
both approaches into a hybrid model, using traces for steady-state into a hybrid approach, using traces during steady-state and
and statistical model for transients. statistical model during transients.
+---------------+
transient | Generate next |
+------>| K_d transient |
+-------------+ / | frames |
R_v(t) | Compare | / +---------------+
------->| against |/
| previous |
| target rate |\
+-------------+ \ +---------------+
\ | Generate next |
+------>| frame from |
steady-state | trace |
+---------------+
Figure 3: Hybrid approach for modeling video traffic
As shown in Figure 3, the video traffic model operates in transient
state if the requested target rate R_v(t) is substantially higher
than the previous target, or else it operates in steady state.
During transient state, a total of K_d frames are generated by the
statistical model, resulting in 1 big burst frame (on average K_r
times larger than average frame size at the target rate) followed by
K_d-1 small frames. When operating in steady-state, the video
traffic model simply generates a frame according to the trace-driven
model given the target rate. One example criteria for determining
whether the traffic model should operate in transient state is
whether the rate increase exceeds 20% of previous target rate.
8. Implementation Status 8. Implementation Status
The statistical model has been implemented as a traffic generator The statistical model has been implemented as a traffic generator
module within the [ns-2] network simulation platform. module within the [ns-2] network simulation platform.
More recently, both the statistical and trace-driven models have been More recently, both the statistical and trace-driven models have been
implemented as a stand-alone traffic source module. This can be implemented as a stand-alone traffic source module. This can be
easily integrated into network simulation platforms such as [ns-2] easily integrated into network simulation platforms such as [ns-2]
and [ns-3], as well as testbeds using a real network. The stand- and [ns-3], as well as testbeds using a real network. The stand-
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