Optimizing personalized interaction experience in crowd-interactive livecast: A cloud-edge approach

Enabling users to interact with broadcasters and audience, the crowd-interactive livecast greatly improves viewer's quality of experience (QoE) and attracts millions of daily active users recently. In addition to striking the balance between resource utilization and viewers' QoE met in the traditional video streaming service, this novel service needs to take supererogatory efforts to improve the interaction QoE, which reflects the viewer interaction experience. To tackle this issue, we conduct measurement studies over a large-scale dataset crawled from a representative livecast service provider. We observe that the individual's interaction pattern is quite heterogeneous: only 10% viewers proactively participate in the interaction, and the rest viewers usually watch passively. Incorporating the insight into the emerging cloud-edge architecture, we propose a framework PIECE, which optimizes the Personalized Interaction Experience with Cloud-Edge architecture (PIECE) for intelligent user access control and livecast distribution. In particular, we first devise a novel deep neural network based algorithm to predict users' interaction intensity using the historical viewer pattern. We then design an algorithm to maximize the individual's QoE, by strategically matching viewer sessions and transcoding-delivery paths over cloud-edge infrastructure. Finally, we use trace-driven experiments to verify the effectiveness of PIECE. Our results show that our prediction algorithm outperforms the state-of-the-art algorithms with a much smaller mean absolute error (40% reduction). Furthermore, in comparison with the cloud-based video delivery strategy, the proposed framework can simultaneously improve the average viewers QoE (26% improvement) and interaction QoE (21% improvement), while maintaining a high streaming bitrate.