path: root/doc/rfc/rfc8593.txt

Internet Engineering Task Force (IETF)                            X. Zhu
Request for Comments: 8593                                       S. Mena
Category: Informational                                    Cisco Systems
ISSN: 2070-1721                                                Z. Sarker
                                                             Ericsson AB
                                                                May 2019


      Video Traffic Models for RTP Congestion Control Evaluations

Abstract

   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 the 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
   model.

Status of This Memo

   This document is not an Internet Standards Track specification; it is
   published for informational purposes.

   This document is a product of the Internet Engineering Task Force
   (IETF).  It represents the consensus of the IETF community.  It has
   received public review and has been approved for publication by the
   Internet Engineering Steering Group (IESG).  Not all documents
   approved by the IESG are candidates for any level of Internet
   Standard; see Section 2 of RFC 7841.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at
   https://www.rfc-editor.org/info/rfc8593.












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Copyright Notice

   Copyright (c) 2019 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
   (https://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Desired Behavior of a Synthetic Video Traffic Model . . . . .   4
   4.  Interactions between Synthetic Video Traffic Source and
       Other Components at the Sender  . . . . . . . . . . . . . . .   5
   5.  A Statistical Reference Model . . . . . . . . . . . . . . . .   7
     5.1.  Time-Damped Response to Target-Rate Update  . . . . . . .   9
     5.2.  Temporary Burst and Oscillation during the Transient
           Period  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     5.3.  Output-Rate Fluctuation at Steady State . . . . . . . . .   9
     5.4.  Rate Range Limit Imposed by Video Content . . . . . . . .  10
   6.  A Trace-Driven Model  . . . . . . . . . . . . . . . . . . . .  10
     6.1.  Choosing the Video Sequence and Generating the Traces . .  11
     6.2.  Using the Traces in the Synthetic Codec . . . . . . . . .  13
       6.2.1.  Main Algorithm  . . . . . . . . . . . . . . . . . . .  13
       6.2.2.  Notes to the Main Algorithm . . . . . . . . . . . . .  14
     6.3.  Varying Frame Rate and Resolution . . . . . . . . . . . .  15
   7.  Combining the Two Models  . . . . . . . . . . . . . . . . . .  16
   8.  Reference Implementation  . . . . . . . . . . . . . . . . . .  17
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  17
   10. Security Considerations . . . . . . . . . . . . . . . . . . .  17
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  17
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  17
     11.2.  Informative References . . . . . . . . . . . . . . . . .  18
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  19









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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.  The
   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.

   On the other hand, evaluation results of a candidate RTP congestion
   control algorithm should mostly reflect the 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 easily duplicated across different candidate
   algorithms.

   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.  The synthetic traffic model should
   also contain tunable parameters so that it can be flexibly adjusted
   to reflect the wide variations in real-world live video encoder
   behaviors.  To this end, this document presents two reference models.
   The first is based on statistical modeling.  The second is driven by
   frame size and interval traces recorded from a real-world encoder.
   This document 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",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "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.











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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's output frame sizes
   sometimes fluctuate 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 (i.e.,
   P-frames in [H264]), the encoder can occasionally generate a large
   intra-coded frame (i.e., I-frame as defined in [H264]) 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 the ability to change frame rate
      and/or spatial resolution or to skip frames upon request.

   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.






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   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  A 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 to low motion).

   These distinct behavior features can be characterized via simple
   statistical modeling or a trace-driven approach.  Sections 5 and 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,
   etc.  Both reference models, as described later in Sections 5 and 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 rate.

   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 necessary.

   o  Target bitrate R_v: Target bitrate request measured in bits per
      second (bps).  Typically, the congestion control module calculates
      the target bitrate and updates it dynamically over time.
      Depending on the congestion control algorithm in use, the update
      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).




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   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 bitrate
      R_v, since it does not make sense to pair very low spatial
      resolutions 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
      when severe packet losses are observed.  This request typically
      comes from the error control module.  It can be initiated either
      by the sender or by the receiver via Full Intra Request (FIR)
      messages as defined in [RFC5104].

   An example of an 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 the 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 content 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.


















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                            +-------------+
                            |             |  dummy encoded
                            |  Synthetic  |   video 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
   traffic source.  Figure 2 summarizes the list of tunable parameters
   in this statistical model.  A more comprehensive survey of popular
   methods for modeling the behavior of video traffic sources can be
   found in [Tanwir2013].




























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     +===========+====================================+================+
     | Notation  | Parameter Name                     | Example Value  |
     +===========+====================================+================+
     | R_v       | Target bitrate 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 KB*     |
     |           | transient period                   |                |
     +-----------+------------------------------------+----------------+
     | t0        | Reference frame interval  1/FPS    |     33 ms      |
     +-----------+------------------------------------+----------------+
     | B0        | Reference frame size  R_v/8/FPS    |    4.17 KB     |
     +-----------+------------------------------------+----------------+
     |           | 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










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5.1.  Time-Damped Response to Target-Rate Update

   While the congestion control module can update its target bitrate
   request R_v at any time, the statistical model dictates that the
   encoder will only react to such changes tau_v seconds after 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 and Oscillation during the Transient Period

   The output bitrate R_o during the period [t, t+tau_v] is considered
   to be in a transient state when reacting to abrupt changes in target
   rate.  Based on observations from video encoder output, the encoder
   reaction to a new target bitrate request can be characterized by high
   variations in output frame sizes.  It is assumed in the model that
   the overall average output bitrate R_o during this transient period
   matches the target bitrate R_v.  Consequently, the occasional burst
   of large frames is 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 the average frame size at
      steady state.

   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 activity level in the video content.

5.3.  Output-Rate Fluctuation at Steady State

   The output bitrate R_o during steady state is modeled as randomly
   fluctuating around the target bitrate R_v.  The output traffic can be
   characterized as the combination of two random processes that denote
   the frame interval t and output frame size B over time, which are the
   two major sources of variations in the encoder output.  For
   simplicity, the deviations of t and B from their respective reference
   levels are modeled as independent and identically distributed (i.i.d)
   random variables following the Laplacian distribution [Papoulis].
   More specifically:






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   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 modeled 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 fluctuation in actual frame intervals (t) with
      respect to the reference frame interval t0.

   o  Fluctuations in frame size: The output-encoded frame sizes also
      tend to fluctuate around the reference frame size B0=R_v/8/FPS.
      Likewise, deviations in the normalized frame size DELTA_B =
      (B-B0)/B0 can be modeled 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 bitrate 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 modeling 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 bitrate 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.




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   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 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.

   Section 6.1 explains the first three steps (1-3), and 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 sequence with content 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 2 to 4 minutes in length sufficiently
   avoids the periodic pattern.

   Given the chosen raw video sequence, denoted "S", one can use a live
   encoder, e.g., some implementation of [H264] or [H265], 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.






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   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
   (see, e.g., [H264] for definitions of I-frames and P-frames).  For
   live encoding, the encoder rate-control algorithm typically does not
   use knowledge of frames in the future when encoding a given frame.

   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 steps.  This
   leads to an encoding bitrate ladder of (n_s + 1) choices equally
   spaced apart by the step length l = (R_max - R_min)/n_s.  The
   following simple algorithm is used to encode the raw video sequence.

                r = R_min
                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 the number of bitrate steps n_s is
   important, since 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; 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.  Section 6.2.2 will discuss
   further the choice of 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.
   Henceforth, for notational clarity, we refer to the bitrate range of
   the trace file as [Rf_min, Rf_max].  The algorithm described in
   Section 6.2.1 also covers the cases when the current target bitrate
   is less than Rf_min or greater than Rf_max.









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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 bitrate request 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 based on R_v as follows:

           R_ref = min (Rf_max, max(Rf_min, R_v))

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

   o  The variable t_current is an index to the frame-size vector stored
      in Traces[r_current].  It is updated every time a new frame is
      due.  It is assumed that all vectors stored in Traces 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
              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 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.





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

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

   a) Rf_min <= R_v < Rf_max:  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 < Rf_min:  The output frame size is calculated via scaling
      with respect to the lowest bitrate Rf_min in the trace file, as
      follows:

             w = R_v / Rf_min
             framesize = max(fs_min, factor * Traces[Rf_min][t_current])

   c) R_v >= Rf_max:  The output frame size is calculated by scaling
      with respect to the highest bitrate Rf_max in the trace file, as
      follows:

                  w = R_v / Rf_max
                  framesize = min(fs_max, w * Traces[Rf_max][t_current])

   In cases b) and c), floating-point arithmetic is used for computing
   the scaling factor "w".  The resulting value of the instantaneous
   frame size (framesize) is further clipped within a reasonable range
   between fs_min (e.g., 10 bytes) and fs_max (e.g., 1 MB).

6.2.2.  Notes to the Main Algorithm

   Note that the 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.




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   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 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, then 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 the choice of fixed frame rate and frame
   resolution.  The model can be 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 experience (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-quantization step size, frame rate, and
   spatial resolution in order to maximize the quality of live video
   codecs [Ozer2011] [Hu2012].  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.






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7.  Combining the Two Models

   It is worthwhile noting that the statistical and trace-driven models
   each have their 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.
   In contrast, it is straightforward for a trace-driven model to obtain
   encoded frame-size data.  Once validated, the statistical model is
   more flexible in mimicking a wide range of encoder/content behaviors
   by simply varying the corresponding 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
   the ongoing steady-state behavior of a video traffic source with
   fluctuations around a constant target rate.  In contrast, the
   statistical model is more versatile for simulating the behavior of a
   video stream in transient, such as when encountering sudden rate
   changes.  It is also possible to combine both methods into a hybrid
   model.  In this case, the steady-state behavior is driven by traces
   during steady state and the transient-state behavior is driven by the
   statistical model.

                                   transient +---------------+
                                     state   | Generate next |
                                     +------>| K_d transient |
               +-----------------+  /        |    frames     |
          R_v  | Compare against | /         +---------------+
        ------>|   previous      |/
               | target bitrate  |\
               +-----------------+ \         +---------------+
                                    \        | 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 a transient
   state if the requested target rate R_v is substantially different
   from the previous target; otherwise, it operates in a 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



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   statistical model.  One example criterion for determining whether the
   traffic model should operate in a transient state is whether the rate
   change exceeds 10% of the 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 as defined in Section 5.1, which is governed
   by parameter tau_v.

8.  Reference Implementation

   The statistical, trace-driven, and hybrid models as described in this
   document have been implemented as a stand-alone, platform-independent
   synthetic traffic source module.  It 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

   This document has no IANA actions.

10.  Security Considerations

   The synthetic video traffic models as described in this document do
   not impose any security threats.  They are designed to mimic
   realistic traffic patterns for evaluating candidate RTP-based
   congestion control algorithms so as to ensure stable operations of
   the network.  It is RECOMMENDED that candidate algorithms be tested
   using the video traffic models presented in this document before wide
   deployment over the Internet.  If the generated synthetic traffic
   flows are sent over the Internet, they also need to be congestion
   controlled.

11.  References

11.1.  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,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.






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11.2.  Informative References

   [H264]     ITU-T, "Advanced video coding for generic audiovisual
              services", Recommendation H.264, April 2017,
              <https://www.itu.int/rec/T-REC-H.264>.

   [H265]     ITU-T, "High efficiency video coding",
              Recommendation H.265, February 2018,
              <https://www.itu.int/rec/T-REC-H.265>.

   [Hu2012]   Hu, H., Ma, Z., and Y. Wang, "Optimization of Spatial,
              Temporal and Amplitude Resolution for Rate-Constrained
              Video Coding and Scalable Video Adaptation", Proc. 19th
              IEEE International Conference on Image Processing (ICIP),
              DOI 10.1109/ICIP.2012.6466960, September 2012.

   [IETF-Interim]
              Zhu, X., Mena, S., and Z. Sarker, "Update on RMCAT Video
              Traffic Model: Trace Analysis and Model Update", IETF
              RMCAT Virtual Interim, April 2017,
              <https://www.ietf.org/proceedings/interim-2017-rmcat-
              01/slides/slides-interim-2017-rmcat-01-sessa-update-on-
              video-traffic-model-draft-00.pdf>.

   [ns-2]     "The Network Simulator - ns-2", December 2015,
              <https://nsnam.sourceforge.net/wiki/index.php/
              User_Information>.

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

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

   [Papoulis] Papoulis, A. and S. Pillai, "Probability, Random Variables
              and Stochastic Processes", London: McGraw-Hill Europe,
              ISBN-13: 978-0071226615, 2002.

   [RFC5104]  Wenger, S., Chandra, U., Westerlund, M., and B. Burman,
              "Codec Control Messages in the RTP Audio-Visual Profile
              with Feedback (AVPF)", RFC 5104, DOI 10.17487/RFC5104,
              February 2008, <https://www.rfc-editor.org/info/rfc5104>.









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   [Syncodecs]
              "Syncodecs: Synthetic codecs for evaluation of RMCAT
              work", commit a92d6c8, May 2018,
              <https://github.com/cisco/syncodecs>.

   [Tanwir2013]
              Tanwir, S. and H. Perros, "A Survey of VBR Video Traffic
              Models", IEEE Communications Surveys and Tutorials, Volume
              15, Issue 4, p. 1778-1802,
              DOI 10.1109/SURV.2013.010413.00071, January 2013.

Authors' Addresses

   Xiaoqing Zhu
   Cisco Systems
   12515 Research Blvd., Building 4
   Austin, TX  78759
   United States of America

   Email: xiaoqzhu@cisco.com


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

   Email: semena@cisco.com


   Zaheduzzaman Sarker
   Ericsson AB
   Torshamnsgatan 23
   Stockholm, SE  164 83
   Sweden

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












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