Abstract

We propose models for variable bit rate (VBR) video traffic that allow for different frame types present in the video, different activity levels of different frames and a variable group of pictures (GOP) structure. We use doubly Markov processes to capture these properties. The performance of these models is evaluated in terms of the stochastic properties of the generated trace as well as using network simulation with five such statistically multiplexed traces. We then illustrate the need to model VBR traces not just at the frame level, but also at lower levels, e.g., at the group of blocks (GOB) level and propose a scheme to partition the frame data generated by our models into these finer hierarchical levels using statistics from training data. 1.