Network Working Group                                             X. Zhu
Internet-Draft                                                   S. Mena
Intended status: Informational                             Cisco Systems
Expires: January 18, 2018                                      Z. Sarker
                                                             Ericsson AB
                                                           July 17, 2017

          Modeling Video Traffic Sources for RMCAT Evaluations


   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

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   This Internet-Draft will expire on January 18, 2018.

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   document authors.  All rights reserved.

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   Provisions Relating to IETF Documents
   ( in effect on the date of

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   publication of this document.  Please review these documents
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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 and oscillation during transient  . . . .   8
     5.3.  Output rate fluctuation at steady state . . . . . . . . .   8
     5.4.  Rate range limit imposed by video content . . . . . . . .   9
   6.  A Trace-Driven Model  . . . . . . . . . . . . . . . . . . . .   9
     6.1.  Choosing the video sequence and generating the traces . .  10
     6.2.  Using the traces in the syntethic codec . . . . . . . . .  11
       6.2.1.  Main algorithm  . . . . . . . . . . . . . . . . . . .  11
       6.2.2.  Notes to the main algorithm . . . . . . . . . . . . .  13
     6.3.  Varying frame rate and resolution . . . . . . . . . . . .  13
   7.  Combining The Two Models  . . . . . . . . . . . . . . . . . .  14
   8.  Implementation Status . . . . . . . . . . . . . . . . . . . .  15
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  15
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  16
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  16
     10.2.  Informative References . . . . . . . . . . . . . . . . .  16
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  17

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 peculiarities 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 how both approaches can be combined.

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   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

   Hence, a synthetic video source should have the following

   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 show a delay in convergence to the target bitrate.

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

   While there exist 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

   In our model, the synthetic video encoder module has a 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: target rate request to the encoder, typically
      from the congestion control module and updated dynamically over
      time.  Depending on the congestion control algorithm in use, the
      update requests can either be periodic (e.g., once per second), or
      on-demand (e.g., only when a drastic bandwidth change over the
      network is observed).

   o  Target frame rate FPS: the instantaneous frame rate measured in
      frames-per-second at a given time.  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: the 2-dimensional vector indicating the
      preferred frame resolution in pixels.  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, since very small
      resolutions do not make sense with very high bitrates, and vice-

   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

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   encoder is capable of outputting.  This typically depends on the
   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 and other
                           modules at the sender

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
   tunable 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          | Target rate request to encoder     |      1 Mbps    |
   | FPS          | Target frame rate of encoder output|     30 Hz      |
   | tau_v        | Encoder reaction latency           |    0.2 s       |
   | K_d          | Burst duration during transient    |      8 frames  |
   | K_B          | Burst frame size during transient  |   13.5 KBytes* |
   | t0           | Reference frame interval  1/FPS    |     33 ms      |
   | B0           | Reference frame size  R_v/8/FPS    |   4.17 KBytes  |
   |              | Scaling parameter of the zero-mean |                |
   |              | Laplacian distribution describing  |                |
   | SCALE_t      | deviations in normalized frame     |    0.15        |
   |              | interval (t-t0)/t0                 |                |
   |              | Scaling parameter of the zero-mean |                |
   |              | Laplacian distribution describing  |                |
   | SCALE_B      | deviations in normalized frame     |    0.15        |
   |              | size (B-B0)/B0                     |                |
   | R_min        | minimum rate supported by video    |    150 Kbps    |
   |              | encoder or content activity        |                |
   | R_max        | maximum rate supported by video    |    1.5 Mbps    |
   |              | encoder or content activity        |                |

   * Example value of K_B for a video stream encoded at 720p and 30 frames
    per second, using H.264/AVC encoder.

    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 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

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   change request at time t, it will simply ignore all subsequent rate
   change requests until time t+tau_v.

5.2.  Temporary burst and 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 high variation in output
   frame 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 of frames in the burst event; and

   o  burst frame size K_B: size of the initial burst frame which is
      typically significantly larger than average frame size at steady

   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
   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
   stream, as well as the video content activity level.

5.3.  Output rate fluctuation at steady state

   We model output rate R_o during steady state as randomly fluctuating
   around the target rate R_v.  The output traffic can be characterized
   as the combination of two random processes denoting the frame
   interval t and output frame size B over time.  These two random
   processes capture two sources of variations in the encoder output:

   o  Fluctuations in frame interval: the intervals between adjacent
      frames have been observed to fluctuate around the reference
      interval of t0 = 1/FPS.  Deviations in normalized frame interval
      DELTA_t = (t-t0)/t0 can be modelled by a zero-mean Laplacian
      distribution with scaling parameter SCALE_t.  The value of SCALE_t
      dictates the "width" of the Laplacian distribution and therefore
      the amount of fluctuations in actual frame intervals (t) with
      respect to the reference t0.

   o  Fluctuations in frame size: size of the output encoded frames also
      tend to fluctuate around the reference frame size B0=R_v/8/FPS.
      Likewise, deviations in the normalized frame size DELTA_B =

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      (B-B0)/B0 can be modelled by a zero-mean Laplacian distribution
      with scaling parameter SCALE_B.  The value of SCALE_B dictates the
      "width" of this second Laplacian distribution and correspondingly
      the amount of fluctuations in output frame sizes (B) with respect
      to the reference target B0.

   Both values of SCALE_t and SCALE_B can be obtained via parameter
   fitting from empirical data captured for a given video encoder.
   Example values are listed in Figure 2 based on empirical data
   presented in [IETF-Interim].

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 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 from the
   congestion control module.

   The following list summarizes the main steps of this approach:

   1) Choose one or more representative raw video sequences.

   2) Encode the sequence(s) using an actual live video encoder.  Repeat
   the process for a number of bitrates.  Keep only the sequence 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

   4) Upon a target bitrate request R_v 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

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

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   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.  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
   sequence whose length is between 2 and 4 minutes 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. 30 fps) and a fixed resolution
   (e.g., 720p).  See section 6.3 for a discussion on how to relax these

   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 when
   encoding a given frame.

   Given R_min and R_max, which are the minimum and maximum bitrates at
   which the synthetic codec is to operate (see Section 4), 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

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   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 whose values are vectors of frame

   The choice of a value for n_s is important, as it determines the
   number of vectors of frame sizes 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 the 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.

   Finally, note that, as mentioned in previous sections, R_min and
   R_max may be modified after the initial sequences are encoded.
   Hence, the algorithm described in the next section also covers the
   cases when the current target bitrate is less than R_min, or greater
   than R_max.

6.2.  Using the traces in the syntethic codec

   The main idea behind the trace-driven synthetic codec is that it
   mimics a real live codec's rate adaptation when the congestion
   controller updates the target rate R_v dynamically.  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 map Traces.  Upon a change
   in the value of R_v, 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 or equal to
   the new value of R_v.  For the moment, we assume the value of R_v to
   be clipped in the range [R_min, R_max].

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

   * 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

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   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
          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 < 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 - r_current ) / l
         framesize = size_hi * distance_lo + size_lo * (1 - distance_lo)

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

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

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

           factor = R_v / 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.

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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-driven synthetic
   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 at time t, it
   will delay any further update to R_v 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 steps of 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-driven 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
   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-
   driven codec to accommodate variable frame rate and/or resolution.

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   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.  Combining The Two Models

   It is worthwhile noting that the statistical and trace-driven models
   each has its own advantages and drawbacks.  While both models are
   fairly simple to implement, it takes significantly greater effort to
   fit the parameters of a statistical model to actual encoder output
   data whereas it is straightforward for a trace-driven model to obtain
   encoded frame size data.  On the other hand, once validated, the
   statistical model is more flexible in mimicking a wide range of
   encoder/content behaviors by simply varying the correponding
   parameters in the model.  In this regard, a trace-driven model relies
   -- by definition -- on additional data collection efforts for
   accommodating new codecs or video contents.

   In general, the trace-driven model is more realistic for mimicking
   ongoing, steady-state behavior of a video traffic source whereas the
   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).  It is also possible to combine both models
   into a hybrid approach, using traces during steady-state and
   statistical model during transients.

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                               transient | Generate next |
                                 +------>| K_d transient |
               +-------------+  /        |    frames     |
        R_v    |   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 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 with size K_B
   followed by K_d-1 smaller frames.  When operating at steady-state,
   the video traffic model simply generates a frame according to the
   trace-driven model given the target rate, while modulating the frame
   interval according to the distribution specified by the statistical
   model.  One example criterion for determining whether the traffic
   model should operate in transient state is whether the rate increase
   exceeds 10% of previous target rate.

8.  Implementation Status

   The statistical model has been implemented as a traffic generator
   module within the [ns-2] network simulation platform.

   More recently, both the statistical and trace-driven models have been
   implemented as a stand-alone traffic source module.  This can be
   easily integrated into network simulation 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

   There are no IANA impacts in this memo.

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10.  References

10.1.  Normative References

   [H264]     ITU-T Recommendation H.264, "Advanced video coding for
              generic audiovisual services", 2003,

   [HEVC]     ITU-T Recommendation H.265, "High efficiency video
              coding", 2015.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

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", in Proc. 19th
              IEEE International Conference on Image
              Processing, (ICIP'12), September 2012.

              Zhu, X., Mena, S., and Z. Sarker, "Update on RMCAT Video
              Traffic Model: Trace Analysis and Model Update", April
              2017, <

   [ns-2]     "The Network Simulator - ns-2",

   [ns-3]     "The Network Simulator - ns-3", <>.

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

              Mena, S., D'Aronco, S., and X. Zhu, "Syncodecs: Synthetic
              codecs for evaluation of RMCAT work",

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

Authors' Addresses

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


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


   Zaheduzzaman Sarker
   Ericsson AB
   Luleae, SE  977 53

   Phone: +46 10 717 37 43

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