Aero: A Pluggable Congestion Control for QUIC
Jashanjot Singh Sidhu, Abdelhak Bentaleb · 2025
QUIC is rapidly emerging as the de-facto standard for HTTP Adaptive Streaming (HAS). QUIC relies on heuristic-based congestion control algorithms which were predominantly designed for TCP and thus have poor generalizability, ultimately degrading the user's Quality of Experience (QoE). Existing learning-based solutions for TCP are not pluggable and require a lot of engineering work, impacting their integration with QUIC. To tackle these challenges, we develop Aero--- the first learning-based plug-and-play congestion control algorithm for QUIC that considers the varying network statistics and can easily be integrated with any QUIC implementation. To analyze the performance of Aero, we conduct a series of comprehensive trace-driven experiments and evaluate its efficiency not only from a congestion control perspective but also the impact it has on the client-driven adaptive bitrate scheme (ABR). Experimental results demonstrate that Aero improves VMAF by ~12% with ~65% less rebuffering for low latency live streaming sessions compared to its competitors. Moreover, Aero excels in terms of delivery rate and delay across different network conditions.