BLADE: Adaptive Wi-Fi Contention Control for Next-Generation Real-Time Communication
This addresses latency issues for users of cloud gaming and XR applications, offering a novel solution to a specific bottleneck in Wi-Fi networks.
The paper tackles the problem of long-tail latency in Wi-Fi networks for next-generation real-time communication applications like cloud gaming, identifying Wi-Fi access points as the bottleneck and proposing BLADE, an adaptive contention control algorithm that reduces Wi-Fi packet transmission tail latency by over 5X and cuts video stall rates by over 90%.
Next-generation real-time communication (NGRTC) applications, such as cloud gaming and XR, demand consistently ultra-low latency. However, through our first large-scale measurement, we find that despite the deployment of edge servers, dedicated congestion control, and loss recovery mechanisms, cloud gaming users still experience long-tail latency in Wi-Fi networks. We further identify that Wi-Fi last-mile access points (APs) serve as the primary latency bottleneck. Specifically, short-term packet delivery droughts, caused by fundamental limitations in Wi-Fi contention control standards, are the root cause. To address this issue, we propose BLADE, an adaptive contention control algorithm that dynamically adjusts the contention windows (CW) of all Wi-Fi transmitters based on the channel contention level in a fully distributed manner. Our NS3 simulations and real-world evaluations with commercial Wi-Fi APs demonstrate that, compared to standard contention control, BLADE reduces Wi-Fi packet transmission tail latency by over 5X under heavy channel contention and significantly stabilizes MAC throughput while ensuring fast and fair convergence. Consequently, BLADE reduces the video stall rate in cloud gaming by over 90%.