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%.

翻译：下一代实时通信（NGRTC）应用，如云游戏和扩展现实（XR），要求持续的超低延迟。然而，通过我们首次大规模测量发现，尽管部署了边缘服务器、专用拥塞控制和丢包恢复机制，云游戏用户在Wi-Fi网络中仍会经历长尾延迟。我们进一步确定，Wi-Fi最后一英里接入点（AP）是主要的延迟瓶颈。具体而言，由Wi-Fi竞争控制标准的基本限制引起的短期数据包传输中断是根本原因。为解决此问题，我们提出了BLADE，一种自适应竞争控制算法，该算法以完全分布式的方式，根据信道竞争水平动态调整所有Wi-Fi发射端的竞争窗口（CW）。我们的NS3仿真及采用商用Wi-Fi AP的实际评估表明，与标准竞争控制相比，BLADE在严重信道竞争下将Wi-Fi数据包传输尾延迟降低了5倍以上，并在确保快速公平收敛的同时显著稳定了MAC吞吐量。因此，BLADE将云游戏中的视频卡顿率降低了90%以上。