A general AR-based technique for the generation of arbitrary gamma VBR video traffic in ATM networks

Modeling variable-bit-rate (VBR) video source traffic is a crucial issue to evaluate the end-to-end performance of transmitting video signals over asynchronous transfer mode (ATM) networks. Difficulties in source modeling arise from the fact that VBR video source traffic usually follows a gamma distribution with high correlation among adjacent data. Many researchers adopt autoregressive (AR) models driven by a Gaussian error process to account for such correlation. The problem is: due to the closure property of the Gaussian distribution, the traffic so generated is Gaussian rather than gamma. As a remedy, some researchers directly consider gamma AR models instead. Unfortunately, the trouble arises from the fact that the closure property does not apply to gamma distributions, and thus, the linear operation performed by an AR model fails to produce gamma traffic. In this paper, we present a new technique that is capable of generating gamma-distributed traffic with arbitrary correlation while retaining the computational efficiency of Gaussian AR models. The central idea is to decompose given gamma traffic into a linear combination of a number of λ²(1) sequences, and each of these latter processes can be easily obtained from a Gaussian AR process through a simple nonlinear operation. Results based on actual video teleconference data are presented to verify the validity of the new algorithm