A Source Model for VBR Video

Statistical evidence suggests that the autocorrelation function of a compressed-video sequence is better captured by (k) = e ? p k than by (k) = k ? = e ? log k (long-range dependence) or (k) = e ?k (Markovian). A video model with such a correlation structure is introduced based on the so-called M=G=1 input processes. Though not Markovian, the model exhibits short-range dependence. Using the queueing performance under`real' video traac as a reference, we study via simulations the queueing performance under two video models: the M=G=1 model and the fractional ARIMA (F-ARIMA) model (which exhibits LRD). Our results indicate that the M=G=1 model is much more accurate in predicting the actual queueing performance than the F-ARIMA model. Furthermore, only O(n) computations are required to generate an M=G=1 trace of length n, compared to O(n 2) for a F-ARIMA trace.