Venus: Enhancing QoE of Crowdsourced Live Video Streaming by Exploiting Multiflow Viewer Assistance
Huanhuan Zhang, Congkai An, Anfu Zhou, Yifan Zhu, Weilin Sun, Yixuan Lu, Jiahao Chen, Liang Liu, Huadóng Ma, Aiguo Fei · 2024
Despite the prevalence of Crowdsourced Live Video Streaming (CLVS), video viewers still suffer from low QoE particularly under rush hours, as the existing Content Delivery Network (CDN) is not scalable enough to handle the massive concurrent streaming. The rapid emergence of Web 3.0 provides new incentives for revisiting and applying the classical P2P networking in CLVS. However, the highly dynamic joining or leaving behavior of CLVS viewers frequently interrupts the real-time streaming and leads to low QoE, which demands to retrofit P2P. In this work, we bridge the gap by proposing a reliable P2P-assisted CLVS system named Venus, where viewers can share their streaming content smoothly, without video freeze regardless of viewers leaving. To realize Venus, different from the single-flow sharing in previous P2P video streaming, we design a novel multiflow framework with lightweight redundancy encoding, so as to handle the inherently high viewer dynamics. Correspondingly, we introduce a multiflow scheduler to enable QoE adaption concertedly over heterogeneous multiple flows. Real-world evaluation confirms the benefits of decentralized CLVS streaming, with Venus outperforming the state-of-the-art CDN solution by almost totally eliminating the video stall while enhancing the video quality by 10.2%.