Flick: Frame-Perceptive Packet Scheduling for Low-Latency Video Services in Wi-Fi Networks

Emerging low-latency video (LLV) services, such as cloud gaming, video conferencing, and virtual reality, demand ultra-low latency for smooth interaction. However, existing methods often overlook the misalignment between frame-level perception and packet-level scheduling in ubiquitous Wi-Fi networks, causing high tail latency and degraded user experience. To this end, we propose Flick, a frame-perceptive packet scheduling framework at Wi-Fi access points (APs). It leverages the periodic per-frame transmission behavior and the LLV traffic distribution characteristics to infer the end-to-end frame latency at APs. Flick consists of three components: a Frame Boundary Identifier that detects video frame boundaries using only packet size, an End-to-End Frame Latency Estimator that estimates the end-to-end latency without sender or receiver timestamps, and a Fast-Send, Slow-Recovery Scheduler that dynamically adjusts scheduling priority based on inferred latency. We implement Flick on a commercial Wi-Fi AP. Testbed results show that Flick reduces P99 latency and stall rate by 57% and 81%, respectively, while preserving 95% throughput and maintaining high fairness.