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The Frame Latency of Personalized Livestreaming Can Be Significantly Slowed Down by WiFi

The popular personalized livestreaming (PL) in China, arguably the largest PL market in the world, is more monetized than PL in US and hence demands much lower interactive latencies to ensure a good quality of user experience. However, our pilot experiment shows that the video frame latency, dominant component of PL's interactive latency, can be significantly slowed down by WiFi, the primary Internet access method for PL. Understanding and further improving the frame latency over WiFi, however, have difficulties in 1) measuring end-to-end latency; 2) parsing encrypted PL's traffic and 3) modeling complex relationships between WiFi radio factors and the latency. To tackle these challenges, we design and prototype Latency Doctor (LTDr), a practical system which aims to model and optimize PL's video frame latency over WiFi. We deploy LTDr in our campus and obtain several key observations based on 13.9M video frames extracted from 12K individual views on three leading PLs in China. We observe that 40% frame latencies over WiFi hop are more than 30ms, and channel utilization should be less than 64% for low latency. Then we build a predictive model based on the dataset using the machine learning methodologies. Two real cases show that the median frame latencies are decreased by LTDr from 130ms to 22ms, and 50ms to 12ms respectively over WiFi networks.