A Frame-Aggregation-Based Approach for Link Congestion Prediction in WiFi Video Streaming

Video streaming using WiFi networks poses the challenge of variable network performance when multiple clients are present. Hence, it is important to continuously monitor and predict the network changes in order to ensure a higher user quality of experience (QoE) for video streaming. Existing approaches that aim to detect such network changes have several disadvantages. For example, active probing approaches are expensive so that generate more additional traffic flow during the testing. To overcome its shortcomings, we propose a passive, lightweight approach, CP-DASH, whereby queuing effects present in frame aggregation are leveraged to predict link congestion in the WiFi network. This approach allows the early detection which can be used to adapt our video appropriately. We conduct experiments simulating a WiFi network with multiple clients and compare CP-DASH with five contemporary rate selection mechanisms. We found that our proposed method significantly reduces the switch rates and stall rates from 22% to 5% and from 38% to 25% compared with an existing throughput-based algorithm, respectively.