Leveraging QoE Heterogenity for Large-Scale Livecaset Scheduling

Abstract

Livecast streaming has received great success in recent years. Although many prior efforts have suggested that dynamic viewer scheduling according to the quality of service (QoS) can improve user engagement, they may suffer inefficiency due to their ignorance of viewer heterogeneity in how the QoS impact quality of experience (QoE). In this paper, we conduct measurement studies over large-scale data provided by a top livecast platform in China. We observe that QoE is influenced by a lot of QoS and non-QoS factors, and most importantly, the QoE sensitivity to QoS metrics can vary significantly among viewers. Inspired by the above insights, we propose HeteroCast, a novel livecast scheduling framework for intelligent viewer scheduling based on viewer heterogeneity. In detail, HeteroCast addresses this concern by solving two sub-problems. For the first sub-problem (i.e., the QoE modeling problem), we use the deep factorization machine (DeepFM) based method to precisely map complicated factors (QoS and non-QoS factors) to QoE and build the QoE model. For the second sub-problem (i.e., the QoE-aware scheduling problem), we use a graph-matching method to generate the best viewer allocation policy for each CDN provider. Specifically, by using some pruning techniques, HeteroCast only introduces slight overhead and can well adapt to the large-scale livecast scenario. Through extensive evaluation on real-world traces, HeteroCast is demonstrated to increase the average QoE by 8.87%-10.09%.