Access control for MPEG video applications using neural network and simulated annealing

We present a dynamic modelfor access control mechanism used in computer communication network applied to MPEG video transmission over Internet. This modelis different fromthosedeveloped inthe previous works related to this topic. In our model, token buckets supported by data buffersare used to shape incoming traffic and one multiplexor, serving all the token pools, multiplexes all theconforming traffic. The model is governed by a system of discrete nonlinear difference equations. Weuse neural network as the feedback controller which receives at its input (measurable) available information and provides at its output the optimal control. The simulated annealing algorithm isusedto optimize the system performance by adjusting the weights. For illustration, we presentnumerical results which show that the system performance of MPEG video server can be improved by using neural network and simulated annealing approach.