Multicast Performance With Hierarchical Cooperation

It has been shown in a previous version of this paper that hierarchical cooperation achieves a linear throughput scaling for unicast traffic, which is due to the advantage of long-range concurrent transmissions and the technique of distributed multiple-input-multiple-output (MIMO). In this paper, we investigate the scaling law for multicast traffic with hierarchical cooperation, where each of the n nodes communicates with k randomly chosen destination nodes. Specifically, we propose a new class of scheduling policies for multicast traffic. By utilizing the hierarchical cooperative MIMO transmission, our new policies can obtain an aggregate throughput of Omega ((n/k)(1-epsilon)) for any epsilon > 0. This achieves a gain of nearly root n/k compared to the noncooperative scheme in Li et al.'s work (Proc. ACM MobiCom, 2007, pp. 266-277). Among all four cooperative strategies proposed in our paper, one is superior in terms of the three performance metrics: throughput, delay, and energy consumption. Two factors contribute to the optimal performance: multihop MIMO transmission and converge-based scheduling. Compared to the single-hop MIMO transmission strategy, the multihop strategy achieves a throughput gain of (n/k)(h-1/h(2h-1)) and meanwhile reduces the energy consumption by k(alpha-2/2) times approximately, where h > 1 is the number of the hierarchical layers, and alpha > 2 is the path-loss exponent. Moreover, to schedule the traffic with the converge multicast instead of the pure multicast strategy, we can dramatically reduce the delay by a factor of about (n/k)(h/2). Our optimal cooperative strategy achieves an approximate delay-throughput tradeoff D(n, k)/T(n,k) = Theta(k) when h -> infinity. This tradeoff ratio is identical to that of noncooperative scheme, while the throughput is greatly improved.