A scene-based generalized Markov chain model for VBR video traffic

The efficient transportation of real-time variable bit rate (VBR) video traffic in high-speed networks has been an area of active research. The VBR video traffic characteristics having heavy tail distribution, high variance and correlation properties are quite complex. These characteristics of VBR video (MPEG) traces are studied and a new traffic model for VBR video is proposed. A modulating Markov chain model is employed in which each state represents the I, B, P frames (pictures) of a group of pictures (GOP). From the video traces, we classify the scenes (collection of GOPs) into high- and low-activity scenes, based on the average number of bits generated during the scenes. The scene activity is modeled by an auxiliary Markov chain wherein each state represents the degree of activity (high/low). The transitions of the auxiliary Markov chain represent scene changes of a video sequence. The bit generation during a low-activity scene is modeled by independent AR processes for I, P, B frames. The cross-correlation with the I frames is taken into account by the AR(1) processes for the P and B frames during the high-activity scenes. The traffic thus generated by the model is analyzed and its characteristics are found to be in close agreement with those exhibited by the real traces. The proposed model is quite flexible in order to model scene changes and the autocorrelation characteristics that are common to all packetized broadcast video sequences. The parameters of the scene changes in the proposed traffic model can be appropriately tuned, so that, even the teleconferencing video traffic that involves few scene changes, can be modeled.