Highlight-Aware Content Placement in Crowdsourced Livecast Services

Abstract

Recent years have witnessed an explosion of crowdsourced livecast (i.e., live broadcast) services, in which any Internet users can act as broadcasters to publish livecasts to fellow viewers. To help grow broadcasters' channels, crowdsourced livecast services provide a past-broadcast saving service, allowing viewers to watch the replays they may have missed. Our real-trace measurement and questionnaire survey show that (1) the duration of most of livecasts is extremely long; (2) a much longer duration largely affects the viewers' Quality-of-Experiences (QoE) when watching the replays. To address this issue and improve viewers' QoE, we propose a crowdsourced framework HighCast based on the interactive messages contributed by the viewers in crowdsourced livecast services. According to a highlight-aware detection module, HighCast can exploit the detection results to schedule the content placement by considering the importance of the predicted streaming highlights. The trace-based evaluations illustrate that the proposed framework improves the prediction accuracy and reduces the viewing latency.