BLADE: Adaptive Wi-Fi Contention Control for Next-Generation Real-Time Communication

TL;DR

BLADE is an adaptive Wi-Fi contention control algorithm that significantly reduces latency and video stalls in real-time cloud gaming by dynamically adjusting contention windows based on channel conditions.

Contribution

This paper introduces BLADE, a novel distributed contention control method that adapts to channel contention to improve latency and stability in Wi-Fi networks for real-time applications.

Findings

BLADE reduces Wi-Fi tail latency by over 5X under heavy contention.

BLADE stabilizes MAC throughput and ensures fast, fair convergence.

BLADE decreases cloud gaming video stall rate by over 90%.

Abstract

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%.