Optimizing the Throughput of Data-Driven Based Streaming in Heterogeneous Overlay Network

Recently, much attention has been paid on data-driven (or swarm-like) based live streaming systems due to its rapid growth in deployment over Internet. In such systems, nodes randomly select their neighbors to form an unstructured overlay mesh (gossip-style overlay construction) and then each node requests desired data blocks from its neighbors (block scheduling). To improve the performance, most of existing works focus on the gossip-style overlay construction issue; however few concentrate on optimizing the block scheduling for improving the throughput of a constructed overlay, especially in heterogeneous environment. In this paper, we propose a scheme to optimize the throughput of data-driven streaming systems in heterogeneous overlay network. We first model the block scheduling problem as a classical min-cost flow problem and thereby derive a global optimal solution. Based on this idea, we then propose DONLE a fully distributed asynchronous scheduling algorithm. Simulation results verify that DONLE is superior to a number of conventional strategies.