Cratus: A Lightweight and Robust Approach for Mobile Live Streaming

Live video applications are getting popular, and content providers widely use adaptive bitrate (ABR) streaming to improve QoE while maintaining low latency. However, users' increasing preference to watch videos on mobile devices poses great challenges for ABR algorithm due to the dramatically varying cellular network. Existing learn-based ABR algorithms face difficulties to generalize to various network conditions because of their reliance on training traces, and model/rule-based ABR schemes suffer from rebuffering under low latency constraint since they cannot robustly control the buffer occupancy within a small range. To address it, this work proposes Cratus, a lightweight and robust ABR algorithm for mobile live streaming, which achieves high QoE and low latency by accurately regulating the buffer at a small level. To enhance the control ability, Cratus controls the buffer dynamic behavior rather than the buffer occupancy. By using sliding mode control approach, Cratus robustly controls the buffer dynamic and ensures that the buffer occupancy is bounded around the target level regardless of network uncertainties. Trace-driven experiments show that Cratus outperforms existing ABRs: average QoE is increased by 12.3% to 28.6%, and rebuffering time is limited within 0.8s on average, which is reduced by 53.5% to 92.3%.