让下一篇重要论文，不再错过。

Interactive virtual reality (VR) streaming over Wi-Fi requires stringent latency and reliability guarantees, which become increasingly difficult to achieve under dynamic channel conditions and shared-medium contention. These challenges make real-time bitrate adaptation a critical yet fundamentally difficult control problem, particularly under limited visibility of network conditions. This paper formulates VR bitrate adaptation as a network-aware, online decision-making problem and proposes BRAVR, a decentralized deep reinforcement learning (DRL) algorithm that combines application-layer observations with lightweight wireless statistics collected at the Wi-Fi access point (AP) to balance visual quality, streaming performance, and multi-user airtime fairness. We implement BRAVR in a real VR streaming system and evaluate it on a physical Wi-Fi testbed against the VR-tailored NeSt-VR algorithm and an ablated BRAVR variant without AP telemetry. Experimental results show that, in the evaluated scenarios, BRAVR consistently achieves its design objectives, delivering robust quality of service (QoS) while avoiding sustained airtime overutilization. It also outperforms the AP-blind ablation, demonstrating the value of incorporating network-level information into the bitrate adaptation control loop. Overall, these results demonstrate the potential of AP-assisted online learning for decentralized interactive VR streaming over commodity Wi-Fi and provide practical insights into bitrate adaptation in shared wireless environments.