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

Video traffic is expected to be the major source for broadband integrated services digital networks (B-ISDN). In this paper we propose two FSM 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 and partition (size-based) classes of video segments. Frames in each segment class produce three datasets one each for I-, B-, and P-type frame. 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 QQ plots to show visual similarity of model-generated VBR video data-sets with original dataset. A leaky-bucket simulation study is 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 even a low data loss rate could affect a large fraction of I frames causing a significant degradation of the quality of transmitted video.