SLVS: A Self-Learning Approach to Achieve Near-Second Low-Latency Video Streaming Under Highly Variable Networks

Fueled by the rapid advances in high-speed mobile networks, live video streaming has seen explosive growth in recent years and some DASH-based algorithms were specifically proposed for low-latency video delivery. We conducted a measurement study for the state-of-the-art algorithms with large-scale network traces. It reveals that these algorithms are susceptible to network condition changes due to the use of solo universal adaptation logics, resulting in the playback latency that has substantial variations across highly fluctuating networks. To tackle this challenge, this paper proposes Stateful Live Video Streaming (SLVS), which is a novel self-learning approach that learns the various network features and optimizes the adaptation logic separately for different network conditions, then dynamically tunes the logic at runtime, so that bitrate decision can better match the changing networks. Moreover, we further generalize SLVS to complement the streaming platform already in service to make it compatible with any live streaming services. Extensive evaluations based on real system prototypes show that SLVS can control playback latency down to 1 s while improving Quality-of-Experience (QoE) by 17.7% to 31.8%. Moreover, it has strong robustness to maintain near-second latency over highly fluctuating networks as well as long periods of video viewing.