Pscheduler: QoE-Enhanced MultiPath Scheduler for Video Services in Large-scale Peer-to-Peer CDNs
Dehui Wei, Jiao Zhang, Haozhe Li, Zhichen Xue, Yajie Peng, Xiaofei Pang, Yuanjie Liu, Rui Han, Jialin Li
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. However, direct applying existing schedulers designed for MPTCP to PCDN fails to meet the two goals of high aggregate bandwidth and low end-to-end delivery latency.
To address this, we present the comprehensive detail of the Douyin self-developed PCDN video transmission system and propose the first QoE-enhanced packet-level scheduler for PCDN systems, called Pscheduler. Pscheduler estimates path quality using a congestion-control-decoupled algorithm and distributes data by the proposed path-pick-packet method to ensure smooth video playback. Additionally, a redundant transmission algorithm is proposed to improve the task download speed for segmented video transmission. Our large-scale online A/B tests, comprising 100,000 Douyin users that generate tens of millions of videos data, show that Pscheduler achieves an average improvement of 60% in goodput, 20% reduction in data delivery waiting time, and 30% reduction in rebuffering rate.
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