上下文感知的自适应P2P流媒体数据调度算法

CONTEXT-AWARE ADAPTIVE DATA SCHEDULING ALGORITHM FOR P2P STREAMING MEDIA

在网格型P2P流媒体系统中,媒体内容通常分成不同的数据块。其中,负责协调来自多个发送节点的数据块的调度算法,是影响用户感知的视频质量的重要因素。为了提高流媒体系统的整体性能,提出一种上下文感知的自适应(CAA)流媒体数据调度算法。算法根据上下文信息定义了数据块的优先级,并动态估计与邻居节点间的网络带宽,根据数据块的优先级、发送节点质量的评估和网络容量等上下文信息计算数据块请求的次序和方向。仿真结果表明,CAA调度算法具有较小的缓冲延迟,在节点吞吐量和系统负载均衡方面比传统的P2P流媒体调度算法有所提高,同时节点连续性指标也得到了改进。

In mesh-based peer-to-peer streaming media systems,media contents are usually divided into different data segments.Among them,the scheduling algorithm in charge of coordinating the data segments from multiple sending peers is the key factor of video quality affecting users＇ perception.In order to improve overall performance of streaming media system,the context-aware adaptive（CAA） streaming media data scheduling algorithm is proposed in this paper.In this algorithm,the priority of data segments is defined based on the context information,and the bandwidth of networks between neighbours＇ nodes is dynamically evaluated.Also,the algorithm calculates the order and direction requested by data segments according to context information such as priority of segments,assessment of sending peers quality and network capacity.Simulation results show that the proposed CAA scheduling algorithm requires smaller buffering delays.What＇s more,it achieves higher peer throughput and more balanced load distribution across peers than the conventional P2P streaming media scheduling algorithms.Meanwhile,it also improves the continuity index of peers.