doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 1 Lightly Compressed Video Traffic Modeling Date: Authors: NameAffiliationsAddressPhone Carlos CordeiroIntel Corp.OR, USA V. Srinivasa SomayazuluIntel Corp.OR, Guoqing LiIntel Corp.OR,

doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 2 Objective As part of the TGad evaluation methodology described in /09-296r6, TGad needs to define a model for lightly compressed video In this presentation we propose a lightly compressed video traffic model that can be used for TGad proposal evaluation

doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 3 Introduction Traffic modeling is an old discipline, but which remains very challenging For the specific case of compressed video traffic, a model has a high dependence on the video source and compression method used Here we derive a model based on a publicly available video source and compression standard, and propose that this model be used by TGad –An alternative model would be to use a random distribution, but even in this case some level of parameter estimation is needed –We believe such model to be reasonable for the purpose of TGad proposal evaluation

doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 4 Video compression in 60GHz (1) The choice of compression technology to meet 60GHz requirements is still being debated However, H.264 is a popular block based compression scheme –Trace data encoded with H.264 is publicly available –Codec algorithm is widely known and reproducible Specific restriction on H.264 codec profiles: only intra-frame coding, no P and B frames –B frames introduce unacceptable latency > 1 frame –P and B frames introduce large memory requirements at decoder – this may be contentious –P and B frames also introduce large error sensitivity: Intra-frame coding is more robust to transmission errors Therefore, for H.264-compressed video at 60GHz, we believe it is more realistic to assume a model whereby only I frames are included Propose to build a model for compressed video using H.264 I-only encoder

doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 5 Video compression in 60GHz (2) To meet the video requirements in r6 for operation in 60GHz, we need to use a modeling method that meets the 2ms latency requirement and buffer constraints That implies that compression at 60GHz will likely be performed at the slice level –Slice-based compression is defined in the H.264 standard itself* –Can define small slice sizes consistent with low latency –E.g., for 1920x1080p video, a slice size of 16*1920*3bytes ~ 92Kbytes (except for the last slice, which is smaller) However, all publicly available compression traces are on a video frame basis We need slice-based statistics Slice 1MB1MB2 MB = Macro-block Slice 2 Slice p Frame * As an example, Cavium Networks ( ) provides a low-latency H.264 based solution using this feature

doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 6 Trace-based modeling A H.264 standards based codec is used to generate slice-based trace data for the lightly video compression modeling –Used the existing CAVLC Intra 4:4:4 H.264 profile Trace data generated from the “Breeze” video clip* –HD 4:4:4 1280x720 30fps movie –1280x16 slice (scaled up to 1920x16 and 60 fps for 1080p60) –Intra-frame-only encoding used: all slices are I slices (no rate control) –Compression ratio of ~ 5.8 for an average bit-rate of ~515Mbps for 1080p60 source Compressed slice statistics No. of slices20745 Min/Max/Ave slice size (Kbytes)6.7 / / 15.8 Variance (Kbytes)1.823 * P. Topiwala, C. Tu, “Introduction to the Viper Dataset,” ISO/IEC JTC1/SC29/WG11 and ITU-T SG16 Q.6, Doc. #JVT- J039, December 2003

doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 7 Proposed compressed video model (1) We plot the PDF of the actual slice size distribution, and the corresponding Normal distribution obtained using the Method of Moments The Normal distribution seems to provide a reasonable approximation to the measured video data –µ = Kbytes –σ = Kbytes

doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 8 Proposed compressed video model (2) The model is based upon one particular video sequence, compressed at one particular bit rate –Therefore, the Normal distribution on the previous slide generates mean bit rates of ~515 Mbps, and a peak bit rate of 693Mbps How to generate slice sizes for different target bit rates ranging up to 3Gbps, while keeping the model simple and avoiding multiple models for different rates? Proposal: Scale the mean of the original Normal distribution according to the desired bit rate –But place an upper bound of Kbytes on the slice size: size cannot exceed the raw data! For example: Desired avg. bit rateNormal distribution for model 0.4*515 Mbps Normal(0.4*µ, 0.4*σ) 2*515 Mbps Normal(2*µ, 2*σ) k*515 Mbps Normal(k*µ, k*σ) ……

doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 9 Proposed compressed video model (3) Parameters –Slice inter-arrival time (IAT) is constant and equal to 1/4080 seconds Since the number of slices per second = (60 fps) * (68 slices per frame) = 4080 –Slice sizes generated with a Normal distribution with µ = Kbytes and σ = Kbytes Yields avg. bit rate (b) = 515 Mbps, peak = 693 Mbps Algorithm –Select target avg. bit rate B, such that B <= 2986 Mbps –At each IAT, generate a slice size with the following distribution: Normal(µ*(B/b), σ*(B/b)) If slice size > Kbytes, set slice size = Kbytes

doc.: IEEE /0709r2 Submission July 2009 Carlos Cordeiro, IntelSlide 10 Conclusions To meet the stringent latency requirements, compressed video transmission in 60GHz will be done on a slice basis A slice-based model has been developed based on publicly available video source and a standards based H.264 codec The model uses a Normal distribution, with parameters that can be scaled to match different bit rates –In practice the proposed Normal distribution may or may not be applicable to other video sources and/or bit rates, but we believe this to be sufficient for TGad purposes Therefore, we propose that the proposed slice-based model be used in the TGad lightly compressed video evaluation methodology Next steps: define target average bit rates for the simulation scenarios in the evaluation methodology