A new QoS provisioning method for adaptive multimedia in cellular wireless networks

Third generation cellular wireless networks are designed to support adaptive multimedia by controlling individual ongoing flows to increase or decrease their bandwidth in response to changes in traffic load. There is growing interest in quality of service (QoS) provisioning under this adaptive Multimedia framework, in which a bandwidth adaptation algorithm needs to be used in conjunction with the call admission control algorithm. This paper presents a novel method for QoS provisioning via the use of the average reward reinforcement learning, which can maximize the network revenue subject to several predetermined QoS constraints. By considering handoff dropping probability, average allocated bandwidth and intra-class fairness simultaneously, our algorithm formulation guarantees that these QoS parameters are kept within predetermined constraints. Unlike other model-based algorithms, our scheme does not require explicit state transition probabilities and therefore the assumptions behind the underlying system model are more realistic than those in previous schemes. Moreover, by considering the status of neighboring cells, the proposed scheme can dynamically adapt to changes in traffic condition. Simulation results demonstrate the effectiveness of the proposed approach in adaptive multimedia cellular networks.