TSMR - a new statistical model for MPEG-coded video

摘要

This paper introduces a new statistical model for VBR video. The idea is to model the variations of the source traffic using a modified Markov-renewal process. The resulting Markov states can be classified into two groups: low-variation and high-variation. A low-variation state corresponds to a small difference between adjacent frames or group of frames, whereas a high-variation state corresponds to a significant change in size. The difference in frame size within each Markov state is then modeled to match both the autocorrelation structure and marginal distribution function. The resulting model is called the two-sided Markov renewal model (TSMR) and is designed specifically for prediction. A simple Markov decision policy is developed and used for this purpose. In order to evaluate this model, real-time prediction is carried out on a number of MPEG coded video streams, and simulation results are discussed and compared with their empirical counterparts. The model is parsimonious in terms of parameters used and memory required. Computation of the prediction algorithm is fast and simple. Only minimal knowledge of the source traffic is required to drive the predictor machine. It is most suitable for the task of dynamic bandwidth allocation in which only very little knowledge about the source is available in advance.