draft-ietf-rmcat-video-traffic-model-01.txt		draft-ietf-rmcat-video-traffic-model-02.txt
	Network Working Group X. Zhu		Network Working Group X. Zhu
	Internet-Draft S. Mena		Internet-Draft S. Mena
	Intended status: Informational Cisco Systems		Intended status: Informational Cisco Systems
	Expires: January 9, 2017 Z. Sarker		Expires: July 12, 2017 Z. Sarker
	Ericsson AB		Ericsson AB
	July 8, 2016		January 8, 2017
	Modeling Video Traffic Sources for RMCAT Evaluations		Modeling Video Traffic Sources for RMCAT Evaluations
	draft-ietf-rmcat-video-traffic-model-01		draft-ietf-rmcat-video-traffic-model-02
	Abstract		Abstract
	This document describes two reference video traffic source models for		This document describes two reference video traffic source models for
	evaluating RMCAT candidate algorithms. The first model statistically		evaluating RMCAT candidate algorithms. The first model statistically
	characterizes the behavior of a live video encoder in response to		characterizes the behavior of a live video encoder in response to
	changing requests on target video rate. The second model is trace-		changing requests on target video rate. The second model is trace-
	driven, and emulates the encoder output by scaling the pre-encoded		driven, and emulates the encoder output by scaling the pre-encoded
	video frame sizes from a widely used video test sequence. Both		video frame sizes from a widely used video test sequence. Both
	models are designed to strike a balance between simplicity,		models are designed to strike a balance between simplicity,
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	Internet-Drafts are working documents of the Internet Engineering		Internet-Drafts are working documents of the Internet Engineering
	Task Force (IETF). Note that other groups may also distribute		Task Force (IETF). Note that other groups may also distribute
	working documents as Internet-Drafts. The list of current Internet-		working documents as Internet-Drafts. The list of current Internet-
	Drafts is at http://datatracker.ietf.org/drafts/current/.		Drafts is at http://datatracker.ietf.org/drafts/current/.
	Internet-Drafts are draft documents valid for a maximum of six months		Internet-Drafts are draft documents valid for a maximum of six months
	and may be updated, replaced, or obsoleted by other documents at any		and may be updated, replaced, or obsoleted by other documents at any
	time. It is inappropriate to use Internet-Drafts as reference		time. It is inappropriate to use Internet-Drafts as reference
	material or to cite them other than as "work in progress."		material or to cite them other than as "work in progress."
	This Internet-Draft will expire on January 9, 2017.		This Internet-Draft will expire on July 12, 2017.
	Copyright Notice		Copyright Notice
	Copyright (c) 2016 IETF Trust and the persons identified as the		Copyright (c) 2017 IETF Trust and the persons identified as the
	document authors. All rights reserved.		document authors. All rights reserved.
	This document is subject to BCP 78 and the IETF Trust's Legal		This document is subject to BCP 78 and the IETF Trust's Legal
	Provisions Relating to IETF Documents		Provisions Relating to IETF Documents
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	publication of this document. Please review these documents		publication of this document. Please review these documents
	carefully, as they describe your rights and restrictions with respect		carefully, as they describe your rights and restrictions with respect
	to this document. Code Components extracted from this document must		to this document. Code Components extracted from this document must
	include Simplified BSD License text as described in Section 4.e of		include Simplified BSD License text as described in Section 4.e of
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	skipping to change at page 2, line 20 ¶		skipping to change at page 2, line 20 ¶
	Table of Contents		Table of Contents
	1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2		1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
	2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3		2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
	3. Desired Behavior of A Synthetic Video Traffic Model . . . . . 3		3. Desired Behavior of A Synthetic Video Traffic Model . . . . . 3
	4. Interactions Between Synthetic Video Traffic Source and		4. Interactions Between Synthetic Video Traffic Source and
	Other Components at the Sender . . . . . . . . . . . . . . . 4		Other Components at the Sender . . . . . . . . . . . . . . . 4
	5. A Statistical Reference Model . . . . . . . . . . . . . . . . 6		5. A Statistical Reference Model . . . . . . . . . . . . . . . . 6
	5.1. Time-damped response to target rate update . . . . . . . 7		5.1. Time-damped response to target rate update . . . . . . . 7
	5.2. Temporary burst/oscillation during transient . . . . . . 7		5.2. Temporary burst and oscillation during transient . . . . 8
	5.3. Output rate fluctuation at steady state . . . . . . . . . 8		5.3. Output rate fluctuation at steady state . . . . . . . . . 8
	5.4. Rate range limit imposed by video content . . . . . . . . 8		5.4. Rate range limit imposed by video content . . . . . . . . 9
	6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 8		6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 9
	6.1. Choosing the video sequence and generating the traces . . 9		6.1. Choosing the video sequence and generating the traces . . 9
	6.2. Using the traces in the syntethic codec . . . . . . . . . 10		6.2. Using the traces in the syntethic codec . . . . . . . . . 11
	6.2.1. Main algorithm . . . . . . . . . . . . . . . . . . . 10		6.2.1. Main algorithm . . . . . . . . . . . . . . . . . . . 11
	6.2.2. Notes to the main algorithm . . . . . . . . . . . . . 12		6.2.2. Notes to the main algorithm . . . . . . . . . . . . . 12
	6.3. Varying frame rate and resolution . . . . . . . . . . . . 12		6.3. Varying frame rate and resolution . . . . . . . . . . . . 13
	7. Combining The Two Models . . . . . . . . . . . . . . . . . . 13		7. Combining The Two Models . . . . . . . . . . . . . . . . . . 14
	8. Implementation Status . . . . . . . . . . . . . . . . . . . . 14		8. Implementation Status . . . . . . . . . . . . . . . . . . . . 15
	9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14		9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
	10. References . . . . . . . . . . . . . . . . . . . . . . . . . 15		10. References . . . . . . . . . . . . . . . . . . . . . . . . . 15
	10.1. Normative References . . . . . . . . . . . . . . . . . . 15		10.1. Normative References . . . . . . . . . . . . . . . . . . 15
	10.2. Informative References . . . . . . . . . . . . . . . . . 15		10.2. Informative References . . . . . . . . . . . . . . . . . 15
	Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 15		Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
	1. Introduction		1. Introduction
	When evaluating candidate congestion control algorithms designed for		When evaluating candidate congestion control algorithms designed for
	real-time interactive media, it is important to account for the		real-time interactive media, it is important to account for the
	characteristics of traffic patterns generated from a live video		characteristics of traffic patterns generated from a live video
	encoder. Unlike synthetic traffic sources that can conform perfectly		encoder. Unlike synthetic traffic sources that can conform perfectly
	to the rate changing requests from the congestion control module, a		to the rate changing requests from the congestion control module, a
	live video encoder can be sluggish in reacting to such changes.		live video encoder can be sluggish in reacting to such changes.
	Output rate of a live video encoder also typically deviates from the		Output rate of a live video encoder also typically deviates from the
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	modules at the sender		modules at the sender
	5. A Statistical Reference Model		5. A Statistical Reference Model
	In this section, we describe one simple statistical model of the live		In this section, we describe one simple statistical model of the live
	video encoder traffic source. Figure 2 summarizes the list of		video encoder traffic source. Figure 2 summarizes the list of
	tunable parameters in this statistical model. A more comprehensive		tunable parameters in this statistical model. A more comprehensive
	survey of popular methods for modelling video traffic source behavior		survey of popular methods for modelling video traffic source behavior
	can be found in [Tanwir2013].		can be found in [Tanwir2013].
	+---------------+--------------------------------+----------------+		+==============+===================================+================+
	| Notation | Parameter Name | Example Value |		| Notation | Parameter Name | Example Value |
	+--------------+---------------------------------+----------------+		+==============+===================================+================+
	| R_v(t) | Target rate request at time t | 1 Mbps |		| R_v(t) | Target rate request at time t | 1 Mbps |
	| R_o(t) | Output rate at time t | 1.2 Mbps |		+--------------+-----------------------------------+----------------+
	| tau_v | Encoder reaction latency | 0.2 s |		| R_o(t) | Output rate at time t | 1.2 Mbps |
	| K_d | Burst duration during transient | 5 frames |		+--------------+-----------------------------------+----------------+
	| K_r | Burst size during transient | 5:1 |		| tau_v | Encoder reaction latency | 0.2 s |
	| R_e(t) | Error in output rate at time t | 0.2 Mbps |		+--------------+-----------------------------------+----------------+
	| SIGMA | standard deviation of normally | 0.1 |		| K_d | Burst duration during transient | 8 frames |
	| | distributed relative rate error | |		+--------------+-----------------------------------+----------------+
	| DELTA | upper and lower bound (+/-) of | 0.1 |		| K_B | Burst frame size during transient | 13.5 KBytes* |
	| | uniformly distributed relative | |		+--------------+-----------------------------------+----------------+
	| | rate error | |		| R_e(t) | Error in output rate at time t | 0.2 Mbps |
	| R_min | minimum rate supported by video | 150 Kbps |		+--------------+-----------------------------------+----------------+
	| | encoder or content activity | |		| SIGMA_t | standard deviation of normalized | |
	| R_max | maximum rate supported by video | 1.5Mbps |		| | frame interval (t/t0) | 0.25 |
	| | encoder or content activity | |		+--------------+-----------------------------------+----------------+
	+--------------+---------------------------------+----------------+		| SIGMA_B | standard deviation of normalized | 0.1 |
			| | frame size (B/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		Figure 2: List of tunable parameters in a statistical video traffic
	source model.		source model.
	5.1. Time-damped response to target rate update		5.1. Time-damped response to target rate update
	While the congestion control module can update its target rate		While the congestion control module can update its target rate
	request R_v(t) at any time, our model dictates that the encoder will		request R_v(t) at any time, our model dictates that the encoder will
	only react to such changes after tau_v seconds from a previous rate		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		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 request at time t, it will simply ignore all subsequent rate
	change requests until time t+tau_v.		change requests until time t+tau_v.
	5.2. Temporary burst/oscillation during transient		5.2. Temporary burst and oscillation during transient
	The output rate R_o during the period [t, t+tau_v] is considered to		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		be in transient. Based on observations from video encoder output
	data, we model the transient behavior of an encoder upon reacting to		data, we model the transient behavior of an encoder upon reacting to
	a new target rate request in the form of largely varying output		a new target rate request in the form of high variation in output
	sizes. It is assumed that the overall average output rate R_o during		frame sizes. It is assumed that the overall average output rate R_o
	this period matches the target rate R_v. Consequently, the		during this period matches the target rate R_v. Consequently, the
	occasional burst of large frames are followed by smaller-than average		occasional burst of large frames are followed by smaller-than average
	encoded frames.		encoded frames.
	This temporary burst is characterized by two parameters:		This temporary burst is characterized by two parameters:
	o burst duration K_d: number frames in the burst event; and		o burst duration K_d: number frames in the burst event; and
	o burst size K_r: ratio of a burst frame and average frame size at
	steady state.		o burst frame size K_B: size of the initial burst frame which is
			typically significantly larger than average frame size at steady
			state.
	It can be noted that these burst parameters can also be used to mimic		It can be noted that these burst parameters can also be used to mimic
	the insertion of a large on-demand I frame in the presence of severe		the insertion of a large on-demand I frame in the presence of severe
	packet losses. The values of K_d and K_r are fitted to reflect the		packet losses. The values of K_d and K_B typically depend on the
	typical ratio between I and P frames for a given video content.		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		5.3. Output rate fluctuation at steady state
	We model output rate R_o as randomly fluctuating around the target		We model output rate R_o as randomly fluctuating around the target
	rate R_v after convergence. There are two variants in modeling the		rate R_v after convergence. There exist two sources of variations in
	random fluctuation R_e = R_o - R_v:		the encoder output:
	o As normal distribution: with a mean of zero and a standard		o Fluctuations in frame interval: the intervals between adjacent
	deviation SIGMA specified in terms of percentage of the target		frames have been observed to fluctuate around the reference
	rate. A typical value of SIGMA is 10 percent of target rate.		interval of t0 = 1/FPS. They roughly follow a Gaussian
			distribution, and can be modelled with the parameter SIGMA_t,
			which denotes the standard deviation of the normalized frame
			interval (ratio between actual and reference frame interval).
	o As uniform distribution bounded between -DELTA and DELTA. A		o Fluctuations in frame size: size of the output encoded frames also
	typical value of DELTA is 10 percent of target rate.		tend to fluctuate around the reference frame size B0=R_v/8/FPS.
			They can also be modelled via a Gaussian distribution, with the
			SIGMA_B denoting the standard deviation of the normalized frame
			size (ratio between actual and reference frame size).
	The distribution type (normal or uniform) and model parameters (SIGMA		Both values of SIGMA_t and SIGMA_B can be obtained via parameter
	or DELTA) can be learned from data samples gathered from a live		fitting from empirical data captured for a given video encoder.
	encoder output.
	5.4. Rate range limit imposed by video content		5.4. Rate range limit imposed by video content
	The output rate R_o is further clipped within the dynamic range		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		[R_min, R_max], which in reality are dictated by scene and motion
	complexity of the captured video content. In our model, these		complexity of the captured video content. In our model, these
	parameters are specified by the application.		parameters are specified by the application.
	6. A Trace-Driven Model		6. A Trace-Driven Model
	skipping to change at page 14, line 25 ¶		skipping to change at page 14, line 47 ¶
	+------>| frame from |		+------>| frame from |
	steady-state | trace |		steady-state | trace |
	+---------------+		+---------------+
	Figure 3: Hybrid approach for modeling video traffic		Figure 3: Hybrid approach for modeling video traffic
	As shown in Figure 3, the video traffic model operates in transient		As shown in Figure 3, the video traffic model operates in transient
	state if the requested target rate R_v(t) is substantially higher		state if the requested target rate R_v(t) is substantially higher
	than the previous target, or else it operates in steady state.		than the previous target, or else it operates in steady state.
	During transient state, a total of K_d frames are generated by the		During transient state, a total of K_d frames are generated by the
	statistical model, resulting in 1 big burst frame (on average K_r		statistical model, resulting in 1 big burst frame with size K_B
	times larger than average frame size at the target rate) followed by		followed by K_d-1 smaller frames. When operating at steady-state,
	K_d-1 small frames. When operating in steady-state, the video		the video traffic model simply generates a frame according to the
	traffic model simply generates a frame according to the trace-driven		trace-driven model given the target rate, while modulating the frame
	model given the target rate. One example criteria for determining		interval according to the distribution specified by the statistical
	whether the traffic model should operate in transient state is		model. One example criteria for determining whether the traffic
	whether the rate increase exceeds 20% of previous target rate.		model should operate in transient state is whether the rate increase
			exceeds 10% of previous target rate.
	8. Implementation Status		8. Implementation Status
	The statistical model has been implemented as a traffic generator		The statistical model has been implemented as a traffic generator
	module within the [ns-2] network simulation platform.		module within the [ns-2] network simulation platform.
	More recently, both the statistical and trace-driven models have been		More recently, both the statistical and trace-driven models have been
	implemented as a stand-alone traffic source module. This can be		implemented as a stand-alone traffic source module. This can be
	easily integrated into network simulation platforms such as [ns-2]		easily integrated into network simulation platforms such as [ns-2]
	and [ns-3], as well as testbeds using a real network. The stand-		and [ns-3], as well as testbeds using a real network. The stand-
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