On Probability Density for Modeling Video Traffic

Abstract

Accurate models for variable bit rate (VBR) video traffic need to allow for different frame types present in the video, different activity levels for different frames, and a variable group of pictures (GOP) structure. The temporal as well as the stochastic properties of the trace data need to be captured by any models. We propose some models that capture temporal properties of the data using doubly Markov processes and autoregressive models. We highlight the importance of capturing the stochastic properties of the data accurately, as this leads to significant improvement in the performance of the model. In order to capture the stochastic properties of the traces, the probability density function of the trace data needs to be accurately modeled. Hence, the focus of this paper is on creating autoregressive processes with arbitrary probability densities. We relate this to work in wavelet theory on the solutions to two-scale dilation equations. The performance of our model is evaluated in terms of the stochastic properties of the generated trace as well as using network simulations.