Rate Adaptive Multimedia Streams: Optimization, Admission Control, and Distributed Algorithms

This work investigates support of rate adaptive multimedia streams on communication networks. Optimal and practical mechanisms to maximize the customer average QoS, defined in terms of a normalized time average received rate, are established. By scaling the arrival rate and link capacity, we obtain asymptotic expressions for customer average QoS in the case of networks with single bottleneck links. The optimal adaptation policy is identified as the solution to an integer program which has an intuitive “sort by volume” interpretation for the case of single bottleneck links. Our asymptotic analysis shows the optimal adaptation policy may yield performance improvements of up to 42% over baseline policies. We demonstrate that a static multi–class admission control policy can achieve the same asymptotic QoS as that of the optimal adaptation policy. This implies that dynamic adaptation may be unnecessary for large capacity networks with appropriate call admission. The multi–class admission policy, however, requires the stream load characteristics be both stationary and known a priori. To address this drawback we investigate a class of distributed algorithms where the frequency of rate adaptations depends on the stream “volume.” We show that these algorithms are able to achieve a QoS comparable to that achieved under the optimal adaptation policy, but without requiring knowledge of system wide parameters. Our simulations indicate our algorithm may yield performance improvements of up to 28% over baseline algorithms. Finally, we investigate using optimal adaptation in a networking environment supporting priority classes for adaptation, e.g., business versus residential customers.