StreamWise: An Intelligent Content Steering for DASH

Multi-CDN strategies have become increasingly important in enhancing Quality of Experience (QoE) for adaptive video streaming. The recent development of the content steering standard (ETSI TS 103 998) aims to facilitate real-time decision-making about the best-performing CDN by gathering statistics from both players and CDNs. However, this task presents significant challenges. Existing solutions for CDN selection rely on heuristics, which often fail to adapt to diverse network conditions and suffer from issues such as prolonged CDN switching delays and/or complex implementation requirements, resulting in poor QoE. To address these limitations, we present StreamWise---a learning-based solution for real-time CDN selection that works effectively for both on-demand and live adaptive video streaming. StreamWise implements on the content steering standard, leveraging a deep reinforcement learning (DRL) framework to learn and predict in real-time the optimal CDN selection policy by continuously interacting with the environment. Our solution adapts in real-time to network conditions, content characteristics, and viewer requirements ensuring the delivery of high-quality content to users. We evaluate StreamWise through extensive trace-driven emulation-based experiments, demonstrating its superior performance compared to conventional CDN selection strategies. Our results demonstrate a significant improvement in VMAF by ~8.5% with ~1.5x higher average bitrate and QoE improvement of ~48% with minimal rebuffering events for video-on-demand streaming and an improvement in VMAF by ~7%, with ~1.2x higher average bitrate and a QoE improvement of ~22% while substantially reducing the rebuffering events by ~10× for live streaming.