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Versions: 00 01 02 draft-ietf-rmcat-video-traffic-model

Network Working Group                                             X. Zhu
Internet-Draft                                                   S. Mena
Intended status: Informational                             Cisco Systems
Expires: January 4, 2016                                       Z. Sarker
                                                             Ericsson AB
                                                            July 3, 2015


          Modeling Video Traffic Sources for RMCAT Evaluations
                draft-zhu-rmcat-video-traffic-source-02

Abstract

   This document describes two reference video traffic source models for
   evaluating RMCAT candidate algorithms.  The first model statistically
   characterizes the behavior of a live video encoder in response to
   changing requests on target video rate.  The second model is trace-
   driven, and emulates the encoder output by scaling the pre-encoded
   video frame sizes from a widely used video test sequence.  Both
   models are designed to strike a balance between simplicity,
   repeatability, and authenticity in modeling the interactions between
   a video traffic source and the congestion control module.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   Internet-Drafts are draft documents valid for a maximum of six months
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   This Internet-Draft will expire on January 4, 2016.

Copyright Notice

   Copyright (c) 2015 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of



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   publication of this document.  Please review these documents
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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Desired Behavior of A Synthetic Video Traffic Model . . . . .   3
   4.  Interactions Between Synthetic Video Traffic Source and
       Other Components at the Sender  . . . . . . . . . . . . . . .   4
   5.  A Statistical Reference Model . . . . . . . . . . . . . . . .   6
     5.1.  Time-damped response to target rate update  . . . . . . .   7
     5.2.  Temporary burst/oscillation during transient  . . . . . .   7
     5.3.  Output rate fluctuation at steady state . . . . . . . . .   8
     5.4.  Rate range limit imposed by video content . . . . . . . .   8
   6.  A Trace-Driven Model  . . . . . . . . . . . . . . . . . . . .   8
     6.1.  Choosing the video sequence and generating the traces . .   9
     6.2.  Using the traces in the syntethic codec . . . . . . . . .  10
       6.2.1.  Main algorithm  . . . . . . . . . . . . . . . . . . .  10
       6.2.2.  Notes to the main algorithm . . . . . . . . . . . . .  12
     6.3.  Varying frame rate and resolution . . . . . . . . . . . .  12
   7.  Comparing and Combining The Two Models  . . . . . . . . . . .  13
   8.  Implementation Status . . . . . . . . . . . . . . . . . . . .  14
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  14
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  14
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  14
     10.2.  Informative References . . . . . . . . . . . . . . . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Introduction

   When evaluating candidate congestion control algorithms designed for
   real-time interactive media, it is important to account for the
   characteristics of traffic patterns generated from a live video
   encoder.  Unlike synthetic traffic sources that can conform perfectly
   to the rate changing requests from the congestion control module, a
   live video encoder can be sluggish in reacting to such changes.
   Output rate of a live video encoder also typically deviates from the
   target rate due to uncertainties in the encoder rate control process.
   Consequently, end-to-end delay and loss performance of a real-time
   media flow can be further impacted by rate variations introduced by
   the live encoder.





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   On the other hand, evaluation results of a candidate RMCAT algorithm
   should mostly reflect performance of the congestion control module,
   and somewhat decouple from pecularities of any specific video codec.
   It is also desirable that evaluation tests are repeatable, and be
   easily duplicated across different candidate algorithms.

   One way to strike a balance between the above considerations is to
   evaluate RMCAT algorithms using a synthetic video traffic source
   model that captures key characteristics of the behavior of a live
   video encoder.  To this end, this draft presents two reference
   models.  The first is based on statistical modelling; the second is
   trace-driven.  The draft also discusses the pros and cons of each
   approach, as well as the possibility to combine both.

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described RFC2119 [RFC2119].

3.  Desired Behavior of A Synthetic Video Traffic Model

   A live video encoder employs encoder rate control to meet a target
   rate by varying its encoding parameters, such as quantization step
   size, frame rate, and picture resolution, based on its estimate of
   the video content (e.g., motion and scene complexity).  In practice,
   however, several factors prevent the output video rate from perfectly
   conforming to the input target rate.

   Due to uncertainties in the captured video scene, the output rate
   typically deviates from the specified target.  In the presence of a
   significant change in target rate, it sometimes takes several frames
   before the encoder output rate converges to the new target.  Finally,
   while most of the frames in a live session are encoded in predictive
   mode, the encoder can occasionally generate a large intra-coded frame
   (or a frame partially containing intra-coded blocks) in an attempt to
   recover from 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
   capabilities:

   o  To change bitrate.  This includes ability to change framerate and/
      or spatial resolution, or to skip frames when required.

   o  To fluctuate around the target bitrate specified by the congestion
      control module.



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   o  To delay in convergence to the target bitrate.

   o  To generate intra-coded or repair frames on demand.

   While there exists many different approaches in developing a
   synthetic video traffic model, it is desirable that the outcome
   follows a few common characteristics, as outlined below.

   o  Low computational complexity: The model should be computationally
      lightweight, otherwise it defeats the whole purpose of serving as
      a substitute for a live video encoder.

   o  Temporal pattern similarity: The individual traffic trace
      instances generated by the model should mimic the temporal pattern
      of those from a real video encoder.

   o  Statistical resemblance: The synthetic traffic should match the
      outcome of the real video encoder in terms of statistical
      characteristics, such as the mean, variance, peak, and
      autocorrelation coefficients of the bitrate.  It is also important
      that the statistical resemblance should hold across different time
      scales, ranging from tens of milliseconds to sub-seconds.

   o  Wide range of coverage: The model should be easily configurable to
      cover a wide range of codec behaviors (e.g., with either fast or
      slow reaction time in live encoder rate control) and video content
      variations (e.g, ranging from high-motion to low-motion).

   These distinct behavior features can be characterized via simple
   statistical models, or a trace-driven approach.  We present an
   example of each in Section 5 and Section 6

4.  Interactions Between Synthetic Video Traffic Source and Other
    Components at the Sender

   Figure 1 depitcs the interactions of the synthetic video encoder with
   other components at the sender, such as the application, the
   congestion control module, the media packet transport module, etc.
   Both reference models, as described later in Section 5 and Section 6,
   follow the same set of interactions.

   The synthetic video encoder takes in raw video frames captured by the
   camera and then dynamically generates a sequence of encoded video
   frames with varying size and interval.  These encoded frames are
   processed by other modules in order to transmit the video stream over
   the network.  During the lifetime of a video transmission session,
   the synthetic video encoder will typically be required to adapt its




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   encoding bitrate, and sometimes the spatial resolution and frame
   rate.

   In our model, the synthetic video encoder module has group of
   incoming and outgoing interface calls that allow for interaction with
   other modules.  The following are some of the possible incoming
   interface calls --- marked as (a) in Figure 1 --- that the synthetic
   video encoder may accept.  The list is not exhaustive and can be
   complemented by other interface calls if deemed necessary.

   o  Target rate R_v(t): requested at time t, typically from the
      congestion control module.  Depending on the congestion control
      algorithm in use, the update requests can either be periodic
      (e.g., once per 1 second), or on-demand (e.g., only when a drastic
      bandwidth change over the network is observed).


   o  Target frame rate FPS(t): the instantaneous frame rate measured in
      frames-per-second at time t.  This depends on the native camera
      capture frame rate as well as the target/preferred frame rate
      configured by the application or user.


   o  Frame resolution XY(t): the 2-dimensional vector indicating the
      preferred frame resolution in pixels at time t.  Several factors
      govern the resolution requested to the synthetic video encoder
      over time.  Examples of such factors are the capturing resolution
      of the native camera; or the current target rate R_v(t), since
      very small resolutions do not make sense with very high bitrates,
      and vice-versa.


   o  Instant frame skipping: the request to skip the encoding of one or
      several captured video frames, for instance when a drastic
      decrease in available network bandwidth is detected.


   o  On-demand generation of intra (I) frame: the request to encode
      another I frame to avoid further error propagation at the
      receiver, if severe packet losses are observed.  This request
      typically comes from the error control module.


   An example of outgoing interface call --- marked as (b) in Figure 1
   --- is the rate range, that is, the dynamic range of the video
   encoder's output rate for the current video contents: [R_min, R_max].
   Here, R_min and R_max are meant to capture the dynamic rate range the
   encoder is capable of outputting.  This typically depends on the



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   video content complexity and/or display type (e.g., higher R_max for
   video contents with higher motion complexity, or for displays of
   higher resolution).  Therefore, these values will not change with
   R_v, but may change over time if the content is changing.


                            +-------------+
                raw video   |             |  encoded video
                 frames     |  Synthetic  |     frames
              ------------> |    Video    | -------------->
                            |   Encoder   |
                            |             |
                            +--------+----+
                                /|\   |
                                 |    |
              -------------------+    +-------------------->
                 interface from          interface to
                other modules (a)       other modules (b)


     Figure 1: Interaction between synthetic video encoder, congestion
                   control, and packet transport module.

5.  A Statistical Reference Model

   In this section, we describe one simple statistical model of the live
   video encoder traffic source.  Figure 2 summarizes the list of tuable
   parameters in this statistical model.  A more comprehensive survey of
   popular methods for modelling video traffic source behavior can be
   found in [Tanwir2013].





















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     +---------------+--------------------------------+----------------+
     | Notation     | Parameter Name                  | Example Value  |
     +--------------+---------------------------------+----------------+
     | R_v(t)       | Target rate request at time t   |      1 Mbps    |
     | R_o(t)       | Output rate at time t           |    1.2 Mbps    |
     | tau_v        | Encoder reaction latency        |    0.2 s       |
     | K_d          | Burst duration during transient |      5 frames  |
     | K_r          | Burst size dur transient        |    5:1         |
     | R_e(t)       | Error in output rate at time t  |    0.2 Mbps    |
     | SIGMA        | standard deviation of normally  |    0.1         |
     |              | distributed relative rate error |                |
     | DELTA        | upper and lower bound (+/-) of  |    0.1         |
     |              | uniformally distributed relative|                |
     |              | rate error                      |                |
     | R_min        | minimum rate supported by video |    150 Kbps    |
     |              | encoder or content activity     |                |
     | R_max        | maximum rate supported by video |   1.5Mbps      |
     |              | encoder or content activity     |                |
     +--------------+---------------------------------+----------------+


    Figure 2: List of tunable parameters in a statistical video traffic
                               source model.

5.1.  Time-damped response to target rate update

   While the congestion control module can update its target rate
   request R_v(t) at any time, our model dictates that the encoder will
   only react to such changes after tau_v seconds from a previous rate
   transition.  In other words, when the encoder has reacted to a rate
   change request at time t, it will simply ignore all subsequent rate
   change requests until time t+tau_v.

5.2.  Temporary burst/oscillation during transient

   The output rate R_o during the period [t, t+tau_v] is considered to
   be in transient.  Based on observations from video encoder output
   data, we model the transient behavior of an encoder upon reacting to
   a new target rate request in the form of largely varying output
   sizes.  It is assumed that the overall average output rate R_o during
   this period matches the target rate R_v.  Consequently, the
   occasional burst of large frames are followed by smaller-than average
   encoded frames.

   This temporary burst is characterized by two parameters:

   o  burst duration K_d: number frames in the burst event; and




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   o  burst size K_r: ratio of a burst frame and average frame size at
      steady state.

   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
   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.

5.3.  Output rate fluctuation at steady state

   We model output rate R_o as randomly fluctuating around the target
   rate R_v after convergence.  There are two variants in modeling the
   random fluctuation R_e = R_o - R_v:

   o  As normal distribution: with a mean of zero and a standard
      deviation SIGMA specified in terms of percentage of the target
      rate.  A typical value of SIGMA is 10 percent of target rate.

   o  As uniform distribution bounded between -DELTA and DELTA.  A
      typical value of DELTA is 10 percent of target rate.

   The distribution type (normal or uniform) and model parameters (SIGMA
   or DELTA) can be learned from data samples gathered from a live
   encoder output.

5.4.  Rate range limit imposed by video content

   The output rate R_o is further clipped within the dynamic range
   [R_min, R_max], which in reality are dictated by scene and motion
   complexity of the captured video content.  In our model, these
   parameters are specified by the application.

6.  A Trace-Driven Model

   We now present the second approach to model a video traffic source.
   This approach is based on running an actual live video encoder
   offline on a set of chosen raw video sequences and using the
   encoder's output traces for constructing a synthetic live encoder.
   With this approach, the recorded video traces naturally exhibit
   temporal fluctuations around a given target rate request R_v(t) from
   the congestion control module.

   The following list summarizes this approach's main steps:

   1) Choose one or more representative raw video sequences.






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   2) Using an actual live video encoder, encode the sequences at
   various bitrates.  Keep just the sequences of frame sizes for each
   bitrate.

   3) Construct a data structure that contains the output of the
   previous step.  The data structure should allow for easy bitrate
   lookup.

   4) Upon a target bitrate request R_v(t) from the controller, look up
   the closest bitrates among those previously stored.  Use the frame
   size sequences stored for those bitrates to approximate the frame
   sizes to output.

   5) The output of the synthetic encoder contains "encoded" frames with
   random contents but with realistic sizes.

   Section 6.1 explains steps 1), 2), and 3), Section 6.2 elaborates on
   steps 4) and 5).  Finally, Section 6.3 briefly discusses the
   possibility to extend the model for supporting variable frame rate
   and/or variable frame resolution.

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
   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
   low-motion sequence that resembles a "talking head", for instance a
   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
   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,
   there will be an obvious periodic pattern in the output frame sizes,
   leading to biased results when evaluating congestion controller
   performance.  In our experience, a one-minute-long sequence is a fair
   tradeoff.

   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
   sequences.  As discussed in Section 3, a live encoder's output
   bitrate can be tuned by varying three input parameters, namely,
   quantization step size, frame rate, and picture resolution.  In order
   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
   (e.g., 480p).  See section 6.3 for a discussion on how to relax these
   assumptions.





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   Following these simplifications, we run the chosen encoder by setting
   a constant target bitrate at the beginning, then letting the encoder
   vary the quantization step size internally while encoding the input
   video sequence.  Besides, we assume that the first frame is encoded
   as an I-frame and the rest are P-frames.  We further assume that the
   encoder algorithm does not use knowledge of frames in the future so
   as to encode a given frame.

   We define R_min and R_max as the minimum and maximum bitrate at which
   the synthetic codec is to operate.  We divide the bitrate range
   between R_min and R_max in n_s + 1 bitrate steps of length l = (R_max
   - R_min) / n_s.  We then use the following simple algorithm to encode
   the raw video sequence.

       r = R_min
       while r <= R_max do
           Traces[r] = encode_sequence(S, r, e)
           r = r + l

   where function encode_sequence takes as parameters, respectively, a
   raw video sequence, a constant target rate, and an encoder algorithm;
   it returns a vector with the sizes of frames in the order they were
   encoded.  The output vector is stored in a map structure called
   Traces, whose keys are bitrates and values are frame size vectors.

   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
   one can choose for n_s is 1, and its maximum value depends on the
   amount of memory available for holding map Traces.  A reasonable
   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.

6.2.  Using the traces in the syntethic codec

   The main idea behind the trace-based synthetic codec is that it
   mimics a real live codec's rate adaptation when the congestion
   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
   needed.

6.2.1.  Main algorithm

   We maintain two variables r_current and t_current:

   * r_current points to one of the keys of the map Traces.  Upon a
   change in the value of R_v(t), typically because the congestion
   controller detects that the network conditions have changed,
   r_current is updated to the greatest key in Traces that is less than



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   or equal to the new value of R_v(t).  For the moment, we assume the
   value of R_v(t) to be clipped in the range [R_min, R_max].

           r_current = r
           such that
              ( r in keys(Traces)  and
              r <= R_v(t)  and
           (not(exists) r' in keys(Traces) such that r < r' <= R_v(t)) )


   * t_current is an index to the frame size vector stored in
   Traces[r_current].  It is updated every time a new frame is due.  We
   assume all vectors stored in Traces to have the same size, denoted
   size_traces.  The following equation governs the update of t_current:


          if t_current < SkipFrames then
              t_current = t_current + 1
          else
              t_current = ((t_current+1-SkipFrames) % (size_traces- SkipFrames))
                          + SkipFrames

   where operator % denotes modulo, and SkipFrames is a predefined
   constant that denotes the number of frames to be skipped at the
   beginning of frame size vectors after t_current has wrapped around.
   The point of constant SkipFrames is avoiding the effect of
   periodically sending a (big) I-frame followed by several smaller-
   than-normal P-frames.  We typically set SkipFrames to 20, although it
   could be set to 0 if we are interested in studying the effect of
   sending I-frames periodically.

   We initialize r_current to R_min, and t_current to 0.

   When a new frame is due, we need to calculate its size.  There are
   three cases:

   a) R_min <= R_v(t) < Rmax:  In this case we use linear interpolation
      of the frame sizes appearing in Traces[r_current] and
      Traces[r_current + l].  The interpolation is done as follows:


     size_lo = Traces[r_current][t_current]
         size_hi = Traces[r_current + l][t_current]
         distance_lo = ( R_v(t) - r_current ) / l
         framesize = size_hi * distance_lo + size_lo * (1 - distance_lo)

   b) R_v(t) < R_min:  In this case, we scale the trace sequence with
      the lowest bitrate, in the following way:



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       factor = R_v(t) / R_min
           framesize = max(1, factor * Traces[R_min][t_current])

   c) R_v(t) >= R_max:  We also use scaling for this case.  We use the
      trace sequence with the greatest bitrate:

       factor = R_v(t) / R_max
           framesize = factor * Traces[R_max][t_current]

   In case b), we set the minimum to 1 byte, since the value of factor
   can be arbitrarily close to 0.

6.2.2.  Notes to the main algorithm

   * Reacting to changes in target bitrate.  Similarly to the
   statistical model presented in Section 5, the trace-based synthetic
   codec has 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, 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 the time
   between frames, i.e. the inverse of the frame rate.

   * 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
   losses.  To implement this extension, the codec's API is augmented
   with a new function to request a new I-frame.  Upon calling such
   function, t_current is reset to 0.

   * Variable length l of steps defined between R_min and R_max.  In the
   main algorithm's description, the step length l is fixed.  However,
   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
   this modification is that the difference between 400 kbps and 600
   kbps as bitrate is much more important than the difference between
   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
   2 Mbps, 400 kbps between 2 Mbps and 3 Mbps, and so on.

6.3.  Varying frame rate and resolution

   The trace-based synthetic codec model explained in this section is
   relatively simple because we have fixed the frame rate and the frame
   resolution.  The model could be extended to have variable frame rate,
   variable spatial resolution, or both.

   When the encoded picture quality at a given bitrate is low, one can
   potentially decrease the frame rate (if the video sequence is
   currently in low motion) or the spatial resolution in order to



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   improve quality-of-experince (QoE) in the overall encoded video.  On
   the other hand, if target bitrate increases to a point where there is
   no longer a perceptible improvement in the picture quality of
   individual frames, then one might afford to increase the spatial
   resolution or the frame rate (useful if the video is currently in
   high motion).

   Many techniques have been proposed to choose over time the best
   combination of encoder quatization step size, frame rate, and spatial
   resolution in order to maximize the quality of live video codecs
   [Ozer2011][Hu2010].  Future work may consider extending the trace-
   based codec to accommodate variable frame rate and/or resolution.

   From the perspective of congestion control, varying the spatial
   resolution typically requires a new intra-coded frame to be
   generated, thereby incurring a temporary burst in the output traffic
   pattern.  The impact of frame rate change tends to be more subtle:
   reducing frame rate from high to low leads to sparsely spaced larger
   encoded packets instead of many densely spaced smaller packets.  Such
   difference in traffic profiles may still affect the performance of
   congestion control, especially when outgoing packets are not paced at
   the transport module.  We leave the investigation of varying frame
   rate to future work.

7.  Comparing and Combining The Two Models

   It is worthwhile noting that the statistical and trace-based models
   each has its own advantages and drawbacks.  Both models are fairly
   simple to implement.  However, it takes significantly more effort to
   fit the parameters of a statistical model to actual encoder output
   data whereas trace-based models does not require such fitting.  On
   the other hand, once validated, the statistical model is more
   flexible in mimicking a wide range of encoder/content behavior by
   simply varying the correponding parameters in the model.  In
   contrast, a trace-driven model relies, by definition, on additional
   data collection efforts for accommodating new codecs or video
   contents.

   In general, trace-based model is more realistic for mimicking
   ongoing, steady-state behavior of a video traffic source whereas
   statistical model is more versatile for simulating transient events
   (e.g., when target rate changes from A to B with temporary bursts
   during the transition).  Therefore, it may be desirable to combine
   both approaches into a hybrid model, using traces for steady-state
   and statistical model for transients.






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8.  Implementation Status

   The statistical model has been implemented as a traffic generator
   module within the [ns-2] network simulation platform.  The trace-
   driven model has been implemented as a stand-alone traffic source
   module which can be easily integrated into the [ns-3] network
   simulation platform.

   Authors of this draft are currently in the process of providing open
   source access to both implementations.

9.  IANA Considerations

   There are no IANA impacts in this memo.

10.  References

10.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, March 1997.

   [H264]     ITU-T Recommendation H.264, "Advanced video coding for
              generic audiovisual services",
              <http://www.itu.int/rec/T-REC-H.264-201304-I>.

   [HEVC]     ITU-T Recommendation H.265, "High efficiency video
              coding", .

10.2.  Informative References

   [Hu2010]   Hu, H., Ma, Z., and Y. Wang, "Optimization of Spatial,
              Temporal and Amplitude Resolution for Rate-Constrained
              Video Coding and Scalable Video Adaptation", inproceedings
              in Proc. 19th IEEE International Conference on Image
              Processing (ICIP'12), September 2012.

   [Ozer2011]
              Ozer, J., "Video Compression for Flash, Apple Devices and
              HTML5", ISBN ISBN-13:978-0976259503, 2011.

   [Tanwir2013]
              Tanwir, S. and H. Perros, "A Survey of VBR Video Traffic
              Models", journal IEEE Communications Surveys and
              Tutorials, vol. 15, no. 5, pp. 1778-1802., October 2013.

   [ns-2]     "The Network Simulator - ns-2",
              <http://www.isi.edu/nsnam/ns/>.



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   [ns-3]     "The Network Simulator - ns-3", <https://www.nsnam.org/>.

Authors' Addresses

   Xiaoqing Zhu
   Cisco Systems
   12515 Research Blvd., Building 4
   Austin, TX  78759
   USA

   Email: xiaoqzhu@cisco.com


   Sergio Mena de la Cruz
   Cisco Systems
   EPFL, Quartier de l'Innovation, Batiment E
   Ecublens, Vaud  1015
   Switzerland

   Email: semena@cisco.com


   Zaheduzzaman Sarker
   Ericsson AB
   Luleae, SE  977 53
   Sweden

   Phone: +46 10 717 37 43
   Email: zaheduzzaman.sarker@ericsson.com






















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