A Large-Scale Measurement and Optimization of Mobile Live Streaming Services

Mobile Live Streaming (MLS) services are one of the most popular types of mobile apps. They involve a (often amateur) user broadcasting content to a potentially large online audience via unreliable networks. Nevertheless, we still lack a deep understanding of MLS user behavior that is critical for optimizing MLS systems, despite some active measurements on viewer-side behavior. Using detailed logs obtained from a major MLS provider, this paper first conducts an in-depth measurement study of both viewer-side and broadcaster-side behavior. Key findings include large wasteful uploads, strong viewing locality, and traffic dominance of loyal viewers. Specifically, 33.3% of uploads go unwatched, and the viewership of broadcasters tends to be localized. Inspired by our findings, we propose
EdgeOpt
– a centralized control center for MLS services for optimizing both the first-mile and the last-mile transmission in MLS. Specifically,
EdgeOpt
reduces wasteful uploading by 71% through adaptive uploading and enhances the replay quality of popular video segments by 10% via highlights retransmission.
EdgeOpt
also uses a learning-based content pre-fetching scheme that boosts the viewing startup by 29.5% and offloads at most 80% of the viewing workload from the edge servers with peer-assisted delivery.