Miguel Casasnovas, Francesc Wilhelmi, Boris Bellalta
原文摘要
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.
Cloud~\ac{VR} imposes stringent latency and reliability requirements due to the closed-loop interaction between \ac{DL} video streaming and \ac{UL} tracking updates. Although Wi-Fi~7 introduces \ac{MLO} to improve throughput and reduce latency, its adoption for latency-critical applications like Cloud~\ac{VR} is not straightforward. This paper investigates Cloud~\ac{VR} streaming over Wi-Fi~7 through a comprehensive cross-layer evaluation and proposes an application-aware \ac{TXS} scheduling policy that exploits the \ac{UL}/\ac{DL} coupling to jointly serve tracking and video traffic. To evaluate our mechanism, we introduce NeXus-VR, an open-source simulator that extends OMNeT++ to integrate the complete Cloud~\ac{VR} application pipeline and the Wi-Fi~7 MAC/PHY layers. Simulation results with multiple \ac{VR} users, \ac{OBSS} contention, and user mobility show that \ac{MLO} and the proposed \ac{TXS} scheduling policy provide complementary benefits. While \ac{MLO} improves transmission opportunities and robustness, the proposed Cloud \ac{VR}-aware \ac{TXS} scheduler preserves tracking freshness by reducing contention between \ac{UL} and \ac{DL} traffic. Together, they increase Cloud~\ac{VR} capacity by up to 2.25$\times$ over conventional single-link operation while consistently reducing latency and improving application-level quality of experience.
Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta
原文摘要
The upcoming IEEE 802.11bn amendment marks a paradigm shift in Wi-Fi, which will pose ambitious performance targets under the paradigm of Ultra-High Reliability (UHR). To understand the implications of such a new technology and to support early research and protocol design for Wi-Fi 8, we present Kom8ndor. This discrete-event network simulator extends the open-source Komondor platform (a simulator validated against ns-3 and other analytical tools) with 802.11bn features. Among the newly added functionalities, we highlight Multi-Access Point Coordination (MAPC)-including Coordinated Time-Division Multiple Access (Co-TDMA), Coordinated Spatial Reuse (Co-SR), and Coordinated Beamforming (Co-BF)-, Non-Primary Channel Access (NPCA), and Dynamic Subband Operation (DSO). Beyond Wi-Fi 8 implementations, Kom8ndor introduces novel functionalities (e.g., a machine learning wrapper for building AI-based protocols) and a modular design to boost the prototyping and research of future Wi-Fi technologies. Kom8ndor is open-source (GNU GPLv3) and available at https://github.com/wn-upf/Komondor.