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Video Traffic Models for RTP Congestion Control Evaluations

The information below is for an old version of the document.
Document Type
This is an older version of an Internet-Draft that was ultimately published as RFC 8593.
Authors Xiaoqing Zhu , Sergio Mena de la Cruz , Zaheduzzaman Sarker
Last updated 2019-02-07 (Latest revision 2018-11-03)
Replaces draft-zhu-rmcat-video-traffic-source
RFC stream Internet Engineering Task Force (IETF)
Additional resources Mailing list discussion
Stream WG state Submitted to IESG for Publication
Document shepherd Colin Perkins
Shepherd write-up Show Last changed 2018-12-10
IESG IESG state Became RFC 8593 (Informational)
Consensus boilerplate Yes
Telechat date (None)
Responsible AD Mirja K├╝hlewind
Send notices to Colin Perkins <>
IANA IANA review state IANA OK - No Actions Needed
Network Working Group                                             X. Zhu
Internet-Draft                                                   S. Mena
Intended status: Informational                             Cisco Systems
Expires: May 7, 2019                                           Z. Sarker
                                                             Ericsson AB
                                                        November 3, 2018

      Video Traffic Models for RTP Congestion Control Evaluations


   This document describes two reference video traffic models for
   evaluating RTP congestion control 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 output of actual encoded video
   frame sizes from a high-resolution test sequence.  Both models are
   designed to strike a balance between simplicity, repeatability, and
   authenticity in modeling the interactions between a live video
   traffic source and the congestion control module.  Finally, the
   document describes how both approaches can be combined into a hybrid

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
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   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on May 7, 2019.

Copyright Notice

   Copyright (c) 2018 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   ( in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
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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 and oscillation during the transient
           period  . . . . . . . . . . . . . . . . . . . . . . . . .   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 synthetic 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 . . . . . . . . . . . . . . . . . . . . .  16
   10. Security Considerations . . . . . . . . . . . . . . . . . . .  16
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  16
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  16
     11.2.  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.

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   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.

   On the other hand, evaluation results of a candidate RTP congestion
   control 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

   One way to strike a balance between the above considerations is to
   evaluate congestion control 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 modeling;
   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
   into a hybrid model.

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

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, the encoder output frame sizes
   sometimes fluctuates for a short, transient period of time before the
   output rate converges to the new target.  Finally, while most of the
   frames in a live session are encoded in predictive mode, the encoder
   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.

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   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.

   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 source 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 modelling, or a trace-driven approach.  Section 5 and
   Section 6 provide an example of each approach, respectively.
   Section 7 discusses how both models can be combined together.

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

   Figure 1 depicts the interactions of the synthetic video traffic
   source with other components at the sender, such as the application,
   the congestion control module, the media packet transport module,

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   etc.  Both reference models --- as described later in Section 5 and
   Section 6 --- follow the same set of interactions.

   The synthetic video source dynamically generates a sequence of dummy
   video frames with varying size and interval.  These dummy 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 source will typically be required to adapt its
   encoding bitrate, and sometimes the spatial resolution and frame

   In this model, the synthetic video source 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 traffic source 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, typically calculated by 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  Target frame resolution XY: the 2-dimensional vector indicating
      the preferred frame resolution in pixels.  Several factors govern
      the resolution requested to the synthetic video source over time.
      Examples of such factors include the capturing resolution of the
      native camera and the display size of the destination screen.  The
      target frame resolution also depends on the current target rate
      R_v, 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.

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   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 [R_min, R_max].  Here, R_min and R_max are
   meant to capture the dynamic rate range and actual live video encoder
   is capable of generating given the input video content.  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

                            |             |  encoded video
                            |  Synthetic  |     frames
                            |    Video    | -------------->
                            |   Source    |
                            |             |
                                /|\   |
                                 |    |
              -------------------+    +-------------------->
                 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

   This section describes 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 modeling video traffic source behavior can be
   found in [Tanwir2013].

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     | Notation  | Parameter Name                     | Example Value  |
     | R_v       | Target rate request                |      1 Mbps    |
     | FPS       | Target frame rate                  |     30 Hz      |
     | tau_v     | Encoder reaction latency           |    0.2 s       |
     | K_d       | Burst duration of the transient    |    8 frames    |
     |           | period                             |                |
     | K_B       | Burst frame size during the        |   13.5 KBytes* |
     |           | transient period                   |                |
     | 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 type or content activity   |                |
     | R_max     | maximum rate supported by video    |    1.5 Mbps    |
     |           | encoder type 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, the statistical model dictates that the
   encoder will only react to such changes tau_v seconds after a

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   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 and oscillation during the transient period

   The output rate R_o during the period [t, t+tau_v] is considered to
   be in a transient state.  Based on observations from video encoder
   output data, the encoder reaction to a new target rate request can be
   characterized by high variation in output frame sizes.  It is assumed
   in the model that the overall average output rate R_o during this
   transient 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

   The output rate R_o during steady state is modelled 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 frame interval 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.

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      Likewise, deviations in the normalized frame size DELTA_B =
      (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 the proposed
   statistical model, these parameters are specified by the application.

6.  A Trace-Driven Model

   The second approach for modelling a video traffic source is trace-
   driven.  This can be achieved by running an actual live video encoder
   on a set of chosen raw video sequences and using the encoder's output
   traces for constructing a synthetic video source.  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 output.

   5.  The output of the synthetic video traffic source contains
       "encoded" frames with dummy contents but with realistic sizes.

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   In the following, Section 6.1 explains the first three steps (1-3),
   Section 6.2 elaborates on the remaining two steps (4-5).  Finally,
   Section 6.3 briefly discusses the possibility to extend the trace-
   driven model for supporting time-varying frame rate and/or time-
   varying frame resolution.

6.1.  Choosing the video sequence and generating the traces

   The first step is a careful choice of a set of video sequences that
   are representative of the target use cases for the video traffic
   model.  For the example use case of interactive video conferencing,
   it is recommended to choose a low-motion sequence that resembles a
   "talking head", e.g. from a news broadcast or recording of an actual
   video conferencing 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 control performance.  It has been empirically
   determined that a sequence with a length between 2 and 4 minutes
   strikes a fair tradeoff.

   Given the chosen raw video sequence, denoted S, one can use a live
   encoder, e.g. some implementation of [H264] or [HEVC], to produce a
   set of encoded sequences.  As discussed in Section 3, the output
   bitrate of the live encoder can be achieved by tuning three input
   parameters: quantization step size, frame rate, and picture
   resolution.  In order to simplify the choice of these parameters for
   a given target rate, one can typically assume a fixed frame rate
   (e.g. 30 fps) and a fixed resolution (e.g., 720p) when configuring
   the live encoder.  See Section 6.3 for a discussion on how to relax
   these assumptions.

   Following these simplifications, the chosen encoder can be configured
   to start at a constant target bitrate, then vary the quantization
   step size (internally via the video encoder rate controller) to meet
   various externally specified target rates.  It can be further assumed
   the first frame is encoded as an I-frame and the rest are P-frames.
   For live encoding, the encoder rate control algorithm typically does
   not use knowledge of frames in the future when encoding a given

   Given the minimum and maximum bitrates at which the synthetic codec
   is to operate (denoted as R_min and R_max, see Section 4), the entire
   range of target bitrates can be divided into n_s + 1 bitrate steps of
   length l = (R_max - R_min) / n_s.  The following simple algorithm is
   used to encode the raw video sequence.

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

   The function encode_sequence takes as input parameters, respectively,
   a raw video sequence (S), a constant target rate (r), and an encoder
   rate control algorithm (e); it returns a vector with the sizes of
   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 sizes.

   The choice of a value for n_s is important, as it determines the
   number of vectors of frame sizes stored in the map Traces.  The
   minimum value one can choose for n_s is 1, and the maximum value
   depends on the amount of memory available for holding the map Traces.
   A reasonable value for n_s is one that results in steps of length l =
   200 kbps.  The next section will discuss further the choice of the
   step length l.

   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 synthetic codec

   The main idea behind the trace-driven synthetic codec is that it
   mimics the rate adaptation behavior of a real live codec upon dynamic
   updates of the target rate R_v by the congestion control module.  It
   does so by switching to a different frame size vector stored in the
   map Traces when needed.

6.2.1.  Main algorithm

   The main algorithm for rate adaptation in the synthetic codec
   maintains two variables: r_current and t_current.

   o  The variable r_current points to one of the keys of map Traces.
      Upon a change in the value of R_v, typically because the
      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.  It is assumed that
      the value of R_v is clipped within the range [R_min, R_max].

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              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))

   o  The variable t_current is an index to the frame size vector stored
      in Traces[r_current].  It is updated every time a new frame is
      due.  It is assumed that all vectors stored Traces to have the
      same size, denoted as 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 large I-frame followed by several smaller-
   than-average P-frames.  A typical value of SkipFrames is 20, although
   it could be set to 0 if one is interested in studying the effect of
   sending I-frames periodically.

   The initial value of r_current is set to R_min, and the initial value
   of t_current set to 0.

   When a new frame is due, its size can be calculated following one of
   the three cases below:

   a) R_min <= R_v < Rmax:  the output frame size is calculated via
      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:  the output frame size is calculated via scaling with
      respect to the lowest bitrate R_min, as follows:

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

   c) R_v >= R_max:  the output frame size is calculated by scaling with
      respect to the highest bitrate R_max:

                  factor = R_v / R_max
                  framesize = factor * Traces[R_max][t_current]

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

6.2.2.  Notes to the main algorithm

   Note that main algorithm as described above can be further extended
   to mimic some additional typical behaviors of a live video encoder.
   Two examples are given below:

   o  I-frames on demand: The synthetic codec can be extended to
      simulate the sending of I-frames on demand, e.g., as a reaction to
      losses.  To implement this extension, the codec's incoming
      interface (see (a) in Figure 1) is augmented with a new function
      to request a new I-frame.  Upon calling such function, t_current
      is reset to 0.

   o  Variable step length l between R_min and R_max: In the main
      algorithm, the step length l is fixed for ease of explanation.
      However, if the range [R_min, R_max] is very wide, it is also
      possible to define a set of intermediate encoding rates with
      variable step length.  The rationale behind this modification is
      that the difference between 400 kbps and 600 kbps as target
      bitrate is much more significant 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 due to fixed frame rate and frame resolution.  The
   model can extended further to accommodate variable frame rate and/or
   variable spatial resolution.

   When the encoded picture quality at a given bitrate is low, one can
   potentially decrease either 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

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   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.

   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 by
   the media transport module.  Investigation of varying frame rate and
   resolution are left for 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.  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 its transient-
   state behavior such as a sudden rate change.  It is also possible to
   combine both methods into a hybrid model, so that the steady-state
   behavior is driven by traces during steady-state and the transient-
   state behavior is driven by the statistical model.

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                               transient +---------------+
                                 state   | Generate next |
                                 +------>| K_d transient |
               +-------------+  /        |    frames     |
        R_v    |   Compare   | /         +---------------+
       ------->|   against   |/
               |   previous  |
               | target rate |\
               +-------------+ \         +---------------+
                                \        | Generate next |
                                 +------>|  frame from   |
                                 steady  |    trace      |
                                 state   +---------------+

                  Figure 3: A hybrid video traffic model

   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 the
   transient state, a total of K_d frames are generated by the
   statistical model, resulting in one (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.  Finally, as this model follows
   transient state behavior dictated by the statistical model, upon a
   substantial rate change, the model will follow the time-damping
   mechanism defined in Section 5.1, which is governed by parameter

8.  Implementation Status

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

   More recently, the statistical, trace-driven, and hybrid models have
   been implemented as a stand-alone, platform-independent 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].

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9.  IANA Considerations

   There are no IANA impacts in this memo.

10.  Security Considerations

   It is important to evaluate RTP-based congestion control schemes
   using realistic traffic patterns, so as to ensure stable operations
   of the network.  Therefore, it is RECOMMENDED that candidate RTP-
   based congestion control algorithms be tested using the video traffic
   models presented in this draft before wide deployment over the

11.  References

11.1.  Normative References

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

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <>.

11.2.  Informative References

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

   [HEVC]     ITU-T Recommendation H.265, "High efficiency video
              coding", April 2013,

   [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, <

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   [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",

              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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