Churn-aware optimal layer scheduling scheme for scalable video distribution in super-peer overlay networks

Abstract

To model a layered video streaming system in super-peer overlay networks that faces with heterogeneity and volatility of peers, we formulate a layer scheduling problem from understanding some constraints such as layer dependency, transmission rule, and bandwidth heterogeneity. To solve this problem, we propose a new layer scheduling algorithm using a real-coded messy genetic algorithm, providing a feasible solution with low complexity in decision. We also propose a peer-utility-based promotion algorithm that selects the most qualified neighbor to guarantee the sustained quality of streaming despite high intensity of churn. Simulation results show that the proposed layer scheduling scheme can achieve the most near-optimal solutions compared to the four conventional scheduling heuristics in the average streaming ratio. It also highly outperforms those with different peer selection strategies in terms of the average bandwidth (6.9 % higher at least) and the variation of utilization (11.3 % lower at least).