The Correlation Structure for a Class of Scene-Based Video Models and Its Impact on the Dimensioning of Video Buffers

We analyze the autocorrelation structure for a class of scene-based MPEG video models at the groups-of-pictures (GOP) (course grain) and frame (fine grain) levels assuming an arbitrary scene-length distribution. At the GOP level, we establish the relationship between the scene-length statistics and the short-range/long-range dependence (SRD/LRD) of the underlying model. We formally show that when the intrascene dynamics exhibit SRD, the overall model exhibits LRD if and only if the second moment of the scene length is infinite. Our results provide the theoretical foundation for several empirically derived scene-based models. We then study the impact of traffic correlations on the packet loss performance at a video buffer. Two popular families of scene-length distributions are investigated: Pareto and Weibull. In the case of Pareto distributed scene lengths, it is observed that the performance is rather insensitive to changes in the buffer size even as the video model enters the SRD regime. For Weibull distributed scene lengths, we observe that for small buffers the loss performance under a frame-level model can be larger than its GOP-level counterpart by orders of magnitude. In this case, the reliance on GOP-level models will result in very optimistic results.