Octopus: Optimizing Interactive Video QoE via Loosely Coupled Codec-Transport Adaptation

Enhancing the quality of experience (QoE) in interactive video streaming (IVS) remains a persistent challenge due to the need for ultra-low latency and rising bandwidth demands. Conventional algorithms, whether rule-based or learning-based, are obsessed with achieving tight coupling between encoding and sending bitrate adaptations for low-latency guarantee. However, our measurement studies reveal alarming harms of tight coupling in suppressing throughput, encoding bitrates and smoothness, as application- and transport-layer bitrate adaptations inherently have different mechanisms and goals. To tackle this problem, we propose Octopus, the first loosely coupled cross-layer bitrate adaptation algorithm for IVS to maximize QoE. Instead of blind synchronization, Octopus promotes mutual cooperation and independence between encoding and sending bitrate adaptations by integrating a multi-head network with shortcut connections and auto-regressive action modules. Additionally, based on meta-imitation reinforcement learning, we design a network condition-aware online adaptation scheme that enables the loosely coupled policy to swiftly adapt to diverse and dynamic wireless networks. We implement Octopus on a testbed, a microcosm of real-world deployment, with transceiver pairs running WebRTC on the WeChat for Business dataset. Results show that Octopus outperforms state-of-the-art algorithms, either improving bitrates by 37.1%, or optimizing stalling rate and smoothness by 54.1% and 9.2%, or achieving all-around improvements.