MPEG-4 spatial scalable encoding scheme (SSES) provides different resolutions and wide range of VBR, (variate bit rate) video to satisfy requirements of end users with variant screen resolutions and network bandwidth. In a SSES, one base layer traffic and one (or more) enhancement layer(s) traffic are generated. Although many traffic models have been developed for single layer video traffic, none exists for VBR, SSES video traffic. Our extensive studies suggest that MMG (Markov-modulated gamma) model can capture base layer traffic quite well. However, it cannot be used for modeling enhancement layer traffic for two reasons: (1) enhancement layers have no I-frames, (2) enhancement layer traffic and base layer traffic are not independent and have a strong correlation. In this paper, we propose two models for enhancement layer traffic and aggregate traffic. To maintain the correlation between base layer and enhancement layers, we augment the base-layer state transition model. The augmentation uses state transition relation between base layer and enhancement layer traffic or between base layer and aggregate traffic. The models have been validated by QQ plots and leaky-bucket simulations. The model generated traffic streams quite accurately mimic original traffic streams.

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.

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.

Assume that n stations, each with a buffer to hold only one packet at a time, are connected as a ring and that data packets are transmitted counterclockwise. A station will attempt to transmit a packet to the next station only if (i) it has a packet to send and (ii) the next station’s buffer is empty. The communication cannels connecting the stations are noisy, and there is a fixed probability $p(0 < p < 1)$ of error-free transmission of a packet from one station to the next in one attempt. An exact expression for the long-run average time for a packet to go around the ring is derived. (A special case of this answers a question raised by Berman and Simon in [Proc. 20th ACM Symp. on Theory of Computing, ACM Press, 1988, pp. 66–77].) For fixed n and p, the throughput of the system is maximum when the number of packets is an integer closest to ${n / 2}$.