Multirate multicast: Optimal algorithms and implementation

Multirate multicast improves user quality but complicates network optimization. This paper introduces a novel control scheme to dynamically optimize multirate multicast. We present MMT, an adaptive policy which combines differential backlog scheduling and intelligent packet dropping, both based on local information. MMT is shown to maximize network throughput by adapting to changing conditions such as channel quality, network congestion, and device capabilities. Then, we study the problem of per-receiver network utility maximization. To maximize sum utility we propose the MMU policy, an extension of MMT with receiver-end flow control. Under the operation of both policies backlog sizes are deterministically bounded, which provides delay guarantees on delivered packets. An important feature of the proposed scheme is that it does not require source cooperation or centralized calculations. To illustrate its practicality, we present a prototype implementation in the NITOS wireless testbed. Experimental results verify the optimality of the scheme and its low complexity.