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 Ω(( [( n)/( k)])^1-ε) for any ε >; 0. This achieves a gain of nearly √{[( n)/( k)]} compared to the noncooperative scheme in Li '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^{[( α-2)/ 2]} times approximately, where h >; 1 is the number of the hierarchical layers, and α >; 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)=Θ(k) when h→ ∞. This tradeoff ratio is identical to that of noncooperative scheme, while the throughput is greatly improved.