Real-Time Video Traffic Prediction and Bandwidth Negotiation Mechanisms for QoS Aware Networks

Abstract

Variable bit-rate (VBR) compressed video traffic is difficult to manage because it has strict delay and loss requirements. In particular, we cannot attain the bandwidth requirements for future frames in real-time video applications. Therefore, it is necessary to use a traffic prediction algorithm to estimate how much bandwidth should be reserved. Up to now, many literatures have proposed many video traffic prediction methods for bandwidth reservation. However, client buffer constraint and delay requirements are not considered in these methods.

In this thesis, we propose a new video traffic prediction scheme and a bandwidth negotiation scheme for real-time video applications. The video traffic prediction scheme is based on picture complexity analysis. The bandwidth negotiation scheme is based on the client buffer size constraint and the predictions from the proposed video traffic prediction scheme. It must decide when to renegotiate its service rate and what the new service rate should be. The performance of the strategy is studied using renegotiated constant bit-rate (RCBR) network service model. Simulation results show that using the proposed prediction scheme for predicting GOP rates reduces the prediction errors from 10% to 40% as compared to the conventional methods. The proposed bandwidth negotiation scheme also achieves high bandwidth utilization with reasonable negotiation times.