Modeling full-length video using Markov-modulated gamma-based framework

All traffic models for MPEG-like encoded variable bit rate (VBR) video can be broadly categorized into (1) data-rate models (DRMs) and (2) frame-size models (FSMs). Almost all proposed VBR traffic models are DRMs. DRMs generate only data arrival rate, and are good for estimating average packet-loss and ATM buffer overflowing probabilities, but fail to identify such details as percentage of frames affected. FSMs generate sizes of individual MPEG frames, and are good for studying frame loss rate in addition to data loss rate. Among three previously proposed FSMs: (1) one generates frame sizes for full-length movies without preserving group-of-pictures (GOP) periodicity; (2) one generates VBR video traffic for news videos from scene content description provided to it; and (3) one generates frame sizes for full-length movies without preserving size-based video-segment transitions. We propose two FSMs that generate frame sizes for full-length VBR videos preserving both GOP periodicity and size-based video-segment transitions. First, two-pass algorithms for analysis of full-length VBR videos are presented. After two-pass analysis, these algorithms identify size-based classes of video shots into which the GOPs are partitioned. Frames in each class produce three data sets, one each for I-, B-, and P-type frames. Each of these data sets is modeled with an axis-shifted gamma distribution. Markov renewal processes model (size-based) video segment transitions. We have used quartile-quartile (QQ) plots to show visual similarity of model-generated VBR video data sets with original data set. Leaky-bucket simulation study has been used to show similarity of data and frame loss rates between model-generated VBR videos and original video. Our study of frame-based VBR video revealed that even a low data-loss rate could affect a large fraction of I frames, causing a significant degradation of the quality of transmitted video.