Look Ahead at the First-mile in Livecast with Crowdsourced Highlight Prediction

Recently, data-driven prediction strategies have shown the potential of shepherding the optimization strategies for end viewer's Quality-of-Experience in practical streaming applications. The current prediction-based designs have largely focused on optimizing the last-mile, i.e., viewer-side, which 1) need the real-time feedback from viewers to improve the prediction accuracy; and 2) need quick responses to guarantee the effectiveness of optimization strategies in the future. Thanks to the emerged crowdsourced livecast services, e.g., Twitch.tv, we for the first time exploit the opportunity to realize the long-term prediction and optimization with the assistance derived from the first-mile, i.e., source broadcasters.

In this paper, we propose a novel framework CastFlag, which analyzes the broadcasters' operations and interactions, predicts the key events (i.e., highlights), and optimizes the transcoding stage in the corresponding live streams, even before the encoding stage. Taking the most popular eSports gamecast as an example, we illustrate the effectiveness of this framework in the game highlight prediction and transcoding workload allocation. The trace-driven evaluation shows the superiority of CastFlag as it:

(1) improves the prediction accuracy over other learning-based approaches by up to 30%; (2) achieves an average of 10% saving of the transcoding latency at less cost.