IEEE 802.16 Presentation Submission Template (Rev. 9) Document Number: Trace Based Streaming Video Traffic Model for 802.16m Evaluation Methodology Document IEEE 802.16 Presentation Submission Template (Rev. 9) Document Number: IEEES80216m-07_179 Date Submitted: 2007-09-10 Source: Ricardo Fricks, Hua Xu email: hua.xu@motorola.com Motorola Inc. 1501 West Shure Drive, Arlington Heights, IL 60004, USA Ronny (Yong-Ho) Kim, Kiseon Ryu email: ronnykim@lge.com LG Electronics Inc. LG R&D Complex, 533 Hogye-1dong, Dongan-gu Anyang, 431-749, Korea Venue: Response to a call for comments and contributions on draft 802.16m Evaluation Methodology Document Base Contribution: None Purpose: Propose to adapt the trace based streaming video traffic model as mandatory model in draft IEEE 802.16m Evaluation Methodology Document. Notice: 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. Release: The contributor grants a free, irrevocable license to the IEEE to incorporate material contained in this contribution, and any modifications thereof, in the creation of an IEEE Standards publication; to copyright in the IEEE’s name any IEEE Standards publication even though it may include portions of this contribution; and at the IEEE’s sole discretion to permit others to reproduce in whole or in part the resulting IEEE Standards publication. The contributor also acknowledges and accepts that this contribution may be made public by IEEE 802.16. Patent Policy: The contributor is familiar with the IEEE-SA Patent Policy and Procedures: <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 >.

Propose to adapt trace based streaming video model as MANDATORY model (1) Trace-based streaming video traffic model represents the key characteristics of streaming video application better than the proposed statistical models Traffic model in the evaluation methodology document (EVM) should focus on capturing the accents of the application which posts special demand on the system performance, In the streaming video traffic model case, the long rang dependency (LRD) is the key characteristic that needs to be captured, because high burstiness resulting from LRD posts high demand on both transport and buffering capability in the system The statistical models proposed in EVM don’t exhibits the LRD characteristic of encoded video traces. (See slides 4-6) Trace-based streaming video traffic model can be easily used for design comparison The trace based streaming video traffic model can be easily duplicated by other system simulators for design comparison No additional statistical variance is introduced by the trace based traffic model itself, which make it easier to compare among results

Propose to adapt trace based streaming video model as MANDATORY model (2) Trace-based streaming video traffic model is widely available and easy to implement Trace based streaming video traffic model an be easily generated by every system simulator (simply read in the trace text file) with no ambiguity It exhibits LRD in an hour trace where large sample size (over 100K samples) will be needed for a statistical model to exhibit LRD There is a public library with 200+ video traces with multiple encoding characteristics (e.g., video quality and resolution) and from diverse video sources (e.g., movies, TV shows, teleconferences, remote learning, cartoons, sporting events). Trace-based steaming video traffic model can represent the general population of the streaming video The trace based streaming video model consists of 12 traces carefully picked from five genre with different data rate, quantization value, hurst parameters, etc. Users (in the simulation) shall pick a trace randomly from the 12 candidates Further more, a random starting point in the trace shall also be picked by the user

Effect of LRD on Network Performance Network performance degrades gradually with increasing LRD (self-similarity). The more self-similar the traffic, the slower the queue length decays. Aggregating streams of self-similar traffic typically intensifies the self-similarity ("burstiness") rather than smoothing it. The bursty behaviour may itself be bursty, which exacerbates the clustering phenomena, and degrades network performance. QoS depends on coping with traffic peaks - video delay bound may be exceeded.

Old school surveys: New modeling trend using traces:

802.16m streaming video model: AR(2) ... but long-range dependence is intrinsic to VBR encoded streams and there is evidence that LRD processes can negatively affect multiplexing performance

Long-Range Dependence Silence of the Lambs Besides the statistical characteristics of the same video stream will depend of many factors: GoP size, quantization, etc Long-Range Dependence

Long-Range Dependence Properties: Encoder: H.264 Full Variable Bit Rate (VBR) Frame Size: CIF 352x288 GoP Size: 16 No. B Frames: 1 Quantizer: 10 Long-Range Dependence Properties: Encoder: H.264 Full Variable Bit Rate (VBR) Frame Size: CIF 352x288 GoP Size: 16 No. B Frames: 1 Quantizer: 16 Long-Range Dependence

Long-Range Dependence Properties: Encoder: H.264 Full Variable Bit Rate (VBR) Frame Size: CIF 352x288 GoP Size: 16 No. B Frames: 1 Quantizer: 48 Long-Range Dependence Short-Range Dependence (?) Properties: Encoder: MPEG4 Rel2 Variable Bit Rate (VBR) Frame Size: (?) 320x240 GoP Size: 12 No. B Frames: 8 Quantizer: (?)