QoE-Optimized MultiPath Scheduling for Video Services in Large-Scale Peer-to-Peer CDNs

Dehui Wei;Jiao Zhang;Xiang Liu;Haozhe Li;Zhichen Xue;Tao Huang;Linshan Jiang;Jialin Li
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Abstract

Video content providers such as Douyin implement Peer-to-Peer Content Delivery Networks (PCDNs) to reduce the costs associated with Content Delivery Networks (CDNs) while still maintaining optimal user-perceived quality of experience (QoE). PCDNs rely on the remaining resources of edge devices, such as edge access devices and hosts, to store and distribute data with a Multiple-Server-to-One-Client (MS2OC) communication pattern. MS2OC parallel transmission pattern suffers from severe data out-of-order issues. PCDNs offer significant cost savings by using multiple low-cost edge devices. However, due to its unique characteristics, including pull-based streaming transmission, many heterogeneous paths, and large receiving buffers, directly applying existing schedulers designed for Multipath TCP (MPTCP) to PCDN fails to meet the two goals of high aggregate bandwidth and low end-to-end delivery latency. To tackle this issue, we provide a detailed overview of Douyin’s self-developed PCDN video transmission system and introduce the first QoE-enhanced packet-level scheduler for PCDN systems, named Pscheduler. Pscheduler evaluates path quality with a congestion-control-decoupled algorithm and employs our proposed path-pick-packet method for data distribution, ensuring a smooth video playback experience. Additionally, we propose a redundant transmission algorithm to enhance task download speeds for segmented video transmission. Our extensive online A/B tests, involving 100,000 Douyin users generating tens of millions of video data points, demonstrate that Pscheduler achieves an average improvement of 60% in goodput, a 20% reduction in data delivery waiting time, and a 30% reduction in rebuffering rates. Furthermore, we conducted simulation experiments that further validate the effectiveness of Pscheduler, confirming its improvements in performance metrics under various network conditions.
大规模对等 CDN 中视频服务的 QoE 优化多路径调度
抖音等视频内容提供商实施点对点内容分发网络(pcdn),以降低与内容分发网络(cdn)相关的成本,同时仍保持最佳的用户感知体验质量(QoE)。pcdn依靠边缘设备(如边缘访问设备和主机)的剩余资源,通过多服务器对一个客户端(ms2c)通信模式来存储和分发数据。MS2OC并行传输模式存在严重的数据乱序问题。pcdn通过使用多个低成本边缘设备,显著节省了成本。然而,由于MPTCP具有pull-based streaming transmission、异构路径多、接收缓冲区大等独特特性,将现有的MPTCP (Multipath TCP)调度程序直接应用到PCDN上,无法满足高聚合带宽和低端到端传输延迟这两个目标。为了解决这个问题,我们详细介绍了抖音自主开发的PCDN视频传输系统,并介绍了首个用于PCDN系统的qos增强包级调度程序Pscheduler。Pscheduler使用拥塞控制解耦算法评估路径质量,并采用我们提出的路径选择数据包方法进行数据分发,确保平滑的视频播放体验。此外,我们提出了一种冗余传输算法来提高分段视频传输的任务下载速度。我们广泛的在线A/B测试,涉及10万抖音用户生成数千万个视频数据点,表明Pscheduler在goodput方面平均提高了60%,数据交付等待时间减少了20%,再缓冲率减少了30%。此外,我们进行了仿真实验,进一步验证了Pscheduler的有效性,确认了其在各种网络条件下性能指标的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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