Abstract
Mobile Live Streaming (MLS) services are now 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 (e.g., LTE). Although prior work has focused on viewer-side behavior, it is equally important to study and improve broadcaster operations. Using detailed logs obtained from a major MLS provider, we first conduct an in-depth measurement study of uploading behavior. Our 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 to a small set of broadcaster-specific network regions. Inspired by our findings, we propose two system innovations to streamline MLS systems: adaptive uploading and edge server pre-fetching. These optimizations leverage machine learning for reduced waste and improved QoE. Trace-driven experiments show that the adaptive uploading reduces the resources wastage by 63%, and the pre-fetching boosts the startup by 29.5%.
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