Sepidar: Incentivized Market-Based P2P Live-Streaming on the Gradient Overlay Network

Live streaming of video content using overlay networks has gained widespread adoption on the Internet. This paper presents Sepidar, a distributed market-based model, that builds and maintains overlay network trees, which are approximately minimal height, for delivering live media as a number of sub streams. A streaming tree is constructed for each sub stream such that nodes that contribute higher amounts of upload bandwidth are located increasingly closer to the media source at the root of the tree. While our distributed market model can be run against a random sample of nodes, we improve its convergence time to stabilize a tree by executing against a sample of nodes that contribute similar amounts of upload bandwidth. We use the Gradient overlay network to generate samples of such nodes. We address the problem of free-riding through parent nodes auditing the behaviour of their child nodes. We evaluate Sepidar by comparing it in simulation with state-of-the-art New Cool streaming. Our results show significantly improved playback latency and playback continuity under churn, flash crowd, and catastrophic failure experiment scenarios. We also show that using the Gradient improves convergence time of our distributed market model compared to a random overlay network. Finally, we show that Sepidar punishes the performance of free-riders, and that nodes are incentivized to contribute more upload bandwidth by relatively improved performance.