Continuous Scheduling for Data-Driven Peer-to-Peer Streaming

Abstract

The paper introduces a new approach to data-driven peer-to-peer media streaming. While classical algorithms send buffer maps and run scheduling periodically, we experiment by continuously sending incremental notifications of buffer contents and by running a continuous scheduling process. The approach has been tested both in a network simulator and in a small network of smart phones with live video content. The protocol seems to adapt quickly to changing data rate or churn without producing intolerable overhead. The approach leads to small latency and strong peer equality and may thus ease neighbor management. The end-to-end delay is small and one may alleviate a need for large buffers.