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%.

The rising crowdsourced live video streaming (CLVS) poses great challenges to Internet transport scalability, where a broadcaster’s live video is expected to reach thousands and even millions of viewers in real time. To accommodate such huge concurrent video traffic, the de-facto solution is to employ content delivery network (CDN), which distributes the traffic spatially relative to end viewers, using geographically distributed servers. However, our measurement study over a top operational CLVS platform reveals that CDN is not scalable enough, i.e., it loses efficacy, particularly duringbusy timeand leads to tremendous QoE degradation, e.g., 33.3% video bitrate reduction, in comparison to networkidle time. In this work, we propose Spider, which revives the peer-to-peer (P2P) networking principle to extend the scalability of CLVS system. Beyond traditional P2P for elastic data transmission, Spider retrofits P2P to meet the stringent low-latency requirements of CLVS: proposing a “pair-push” streaming mode to tame the excessive signaling latency; designing a QoE-driven peer pairing algorithm to tackle the Internet path variation and CLVS viewer dynamics. We implement, deploy and evaluate Spider in real-world over 20.9 thousand video sessions. Compared to the de-facto CDN solution, Spider achieves remarkable gains, e.g., video stall rate reductions of 52.57%, video quality gains of 8.22%, and even 66% CDN bandwidth saving. The results validate the feasibility and practicability of embracing P2P for low-latency live video communication for the first time.

Face image animation generates a synthetic human face video that harmoniously integrates the identity derived from the source image and facial motion obtained from the driving video. This technology could be beneficial in multiple medical fields, such as diagnosis and privacy protection. Previous studies on face animation often relied on a single source image to generate an output video. With a significant pose difference between the source image and the driving frame, the quality of the generated video is likely to be suboptimal because the source image may not provide sufficient features for the warped feature map. In this study, we propose a novel face-animation scheme based on multiple sources and perspective alignment to address these issues. We first introduce a multiple-source sampling and selection module to screen the optimal source image set from the provided driving video. We then propose an inter-frame interpolation and alignment module to further eliminate the misalignment between the selected source image and the driving frame. The proposed method exhibits superior performance in terms of objective metrics and visual quality in large-angle animation scenes compared to other state-of-the-art face animation methods. It indicates the effectiveness of the proposed method in addressing the distortion issues in large-angle animation.