A survey of statistical source models for variable-bit-rate compressed video

It is predicted that, in the near future, the transport of compressed video will pervade computer networks. Variable-bit-rate (VBR) encoded video is e xpected to become a significant source of network traffic, due to its advan tages in statistical multiplexing gain and consistent video quality. Both s ystems analysts and developers need to assess and study the impact these so urces will have on their networks and networking products. To this end, sui table statistical source models are required to analyze performance metrics such as packet loss, delay and jitter. This paper provides a survey of VER source models which can be used to drive network simulations. The models a re categorized into four groups: Markov chain/linear regression, TES, self- similar and i.i.d/analytical. We present models which have been used for VE R sources containing moderate-to-significant scene changes and moderate-to- full motion. A description of each model is given along with corresponding advantages and shortcomings. Comparisons are made based on the complexity o f each model.