Nested auto-regressive processes for MPEG-encoded video traffic modeling

This paper presents a new traffic model for MPEG-encoded video sequences. T he hybrid Gamma/Pareto distribution is used for all three types of frames i n MPEG-encoded video sequences, and the present model takes scene changes i nto account. The autocorrelation structure is modeled using two second-orde r auto-regressive (AR) processes nested with each other. One AR process is used to generate the mean frame size of the scenes to model the long-range dependence, and another AR process is used to generate the fluctuations wit hin the scenes to model the short range dependence. The parameters of the A R processes are estimated from measurements of empirical video sequences. S imulation results show that the present model captures the autocorrelation structure in the empirical traces at both small and large lags. The MPEG tr affic model presented in this paper is used to predict the queueing perform ance of single and multiplexed MPEG video sequences at an asynchronous tran sfer mode multiplexer, Comparison study shows that the present model provid es accurate prediction for quality of service measures, such as cell-loss r atio under different traffic loads and various buffer sizes.