IEEE C802.16m-07/145
Project
IEEE 802.16 Broadband Wireless Access Working Group <http://ieee802.org/16>
Title
Trace Based Streaming Video Traffic Model for 802.16m Evaluation Methodology
Document
Date
Submitted
2007-07-09
Source(s)
Ricardo Fricks, Hua Xu
Motorola, Inc.
1501 West Shure Drive,
Arlington Heights, IL 60004, USA
E-mail: hua.xu@motorola.com
Jeongeun Julie Lee, Belal Hamzeh
Intel Corp.
2111 NE 25th Ave,
Hillsboro, Oregon 97124, USA
E-mail: jeong.julie.lee@intel.com
Ronny (Yong-Ho) Kim, Kiseon Ryu
LG Electronics Inc.
LG R&D Complex, 533 Hogye-1dong,
Dongan-gu, Anyang, 431-749, Korea
E-mail: ronnykim@lge.com
Re:
Response to a call for comments and contributions on draft 802.16m Evaluation Methodology
Document
Abstract
In this document, a trace based streaming video traffic model is proposed.
Purpose
Propose a trace based streaming video traffic model in the IEEE 802.16m draft Evaluation
Methodology Document.
Notice
Release
Patent
Policy
This document does not represent the agreed views of the IEEE 802.16 Working Group or any of its subgroups. It
represents only the views of the participants listed in the “Source(s)” field above. It is offered as a basis for
discussion. It is not binding on the contributor(s), who reserve(s) the right to add, amend or withdraw material
contained herein.
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<http://standards.ieee.org/guides/bylaws/sect6-7.html#6> and
<http://standards.ieee.org/guides/opman/sect6.html#6.3>.
Further information is located at <http://standards.ieee.org/board/pat/pat-material.html> and
<http://standards.ieee.org/board/pat>.
Trace Based Streaming Video Traffic Model
Ricardo Fricks1, Hua Xu1, Jeongeun Julie Lee2, Belal Hamzeh2,
Ronny (Yong-Ho) Kim3, Kiseon Ryu3
1
Motorola, Inc. hua.xu@motorola.com
Intel Corp. jeong.julie.lee@intel.com
2
1
IEEE C802.16m-07/145
LG Electronics Inc. ronnykim@lge.com
3
The following slides provide the technical detail in supporting the changes indicated below.
D:\Profiles\qa1359\
My Documents\2007_Strat\802_16m\Channel_Model_Methodology\traffic_model\802_16_TraceBased_Streaming_Video_Model.ppt
[Replace 10.4 with the following.]
10.4. Near Real Time Video Streaming
There is no silver bullet on the synthetic traffic generation for streaming video. Multiple analytical algorithms
are proposed but no single reference algorithm is ideal for the task. Generally, long rang dependency is
recognized for the probability distributions of frame sizes. By using self-similar traffic generator, some of the
characteristics of the streaming video traffic can be reproduced. However, the synthetic video traces generated
by the analytical model are so much dissimilar to the reference traces that it is difficult to convince people that
synthetic traces have captured the core characteristics of the streaming video traffic.
Since the streaming video traces are easy to get and they are easy to use in the simulation environment, a trace
based streaming video traffic model is recommended. In this model, a set of 12 MPEG4 traces are selected from
the ASU video library. They are representative of the typical mix supported in the network. Among the 12
traces, 6 of them are from the major movie genre, such as drama, action, SciFi, and carton; 2 of them are from
major sport events; 1 is from MTV, 2 are from talk show with and without commercial, and 1 is from TV
sitcom. The key characters of these streaming video traces are listed in the table below.
MPEG4 Video Library*
Name
Hurst
Mean Bit
Quantizatio CBR/V
Parameter Rate (Kbps) n (I-P-B)
BR
Movie
1
2
3
4
5
6
Citizen Kane
Citizen Kane
Die Hard
Jurassic Park
Star War IV
Aladdin
0.84
30-30-30
0.72
0.61
0.78
0.86
52
128
70
78.5
65
91
30-30-30
24-24-24
24-24-24
30-30-30
VBR
CBR
VBR
VBR
VBR
VBR
0.74
267.5
24-24-24
VBR
0.58
74.2
30-30-30
VBR
0.85
212.4
24-24-24
VBR
Sports
Football With
7 Commercials
Baseball With
8 Commercials
MTV
9 MTV
Talk Show
Tonight Show With
10 Commercials (Jay Leno)
Tonight Show Without
11 Commercials (Jay Leno)
0.8
482
24-24-24
VBR
0.93
55
24-24-24
VBR
12 Friends vol4
0.77
53
24-24-24
VBR
Sitcom
* From ASU video library. URL: http://trace.eas.asu.edu/
2