A video on demand (VoD) system is expected to transmit many movies over a single channel as demanded by the end users. When several videos are transmitted simultaneously over a link the effective bandwidth required per video is usually much lower than that needed by a single video because of multiplexing gain. Fast and accurate estimation of multiplexing gain is necessary for developing call admission control (CAC) algorithms. Known models which estimate queue size and effective bandwidth of multiplexed video system cannot capture frame size variations in different segments of a video, and are not very useful particularly when the number of videos is not too large. The multinomial model proposed in this paper is built on a Markov-modulated gamma (MMG)-based traffic model of a single video. It takes into consideration average frame size variations in different segments of a video and can predict multiplexing gain for any number of multiplexed videos. The model has been validated using MPEG traces of commercial movies.

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.

MPEG-encoded variable bit rate (VBR) video traffic is expected to incur cell losses during its transmission over a broadband network. These cell losses introduce distortion into the received video. Frame drop rate and root mean square error (RMSE) can be used to objectively measure this distortion. These two metrics are useful in identifying the threshold cell loss rate (CLR) beyond which the quality of received video is unacceptable to viewers. Thus, they are useful in identifying CLRs to be used in evaluating VBR video traffic models. A novel analytical model is developed for predicting both frame drop rate and RMSE as a function of (i) uniform cell loss rate and (ii) MPEG encoding parameters. The model is validated by an empirical study using an actual MPEG video.

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.