大规模客户端视频流带宽调度

Video Streaming Bandwidth Scheduling for Large-scale Client

视频流服务的迅猛发展,大规模用户共享带宽链路的场景不断增多.现存的DASH视频流采用的ABR算法多用于提高单客户端用户的体验质量(quality of experience,QoE),还有一些算法仅针对数个客户端的情况.本文提出一种应用于大规模客户端场景的带宽调度算法,通过聚类算法减小调度规模,再将带宽分配同ABR算法结合,对聚类客户端进行比特率决策,提高带宽利用率,保证总体QoE最大化.我们的实验结果表明,与均分带宽的调度方式相比,通过对聚类客户端带宽调度并应用到所有客户端的方式总用户QoE提升99.4%,相比于最先进的Minerva方案总QoE提高10.7%.

With the rapid development of video streaming services,scenarios in which large-scale users share bandwidth links increase unceasingly.The existing adaptive bitrate(ABR)algorithm used in dynamic adaptive streaming over HTTP(DASH)video streaming is mostly used to improve the quality of experience(QoE)of single-client customers,while some other algorithms are only for multi-client situations.This study proposes a bandwidth scheduling algorithm for large-scale client situations.A clustering algorithm is adopted to reduce the scheduling scale.Then,bandwidth allocation is combined with the ABR algorithm to make bitrate decisions for clustering clients and thereby to improve bandwidth utilization and ensure a maximum overall QoE.The experimental results show that compared with the bandwidth-sharing scheduling method,the method of scheduling the clustering client bandwidth and applying it to all clients achieves a99.4%increase in overall user Qo E.The overall Qo E increase is 10.7%on that of the best state-of-the-art scheme Minerva.