A Frame-Aggregation-Based Approach for Link Congestion Prediction in WiFi Video Streaming
A Frame-Aggregation-Based Approach for Link Congestion Prediction in WiFi Video Streaming
复制标题
DOI：
10.1109/icccn49398.2020.9209675
复制
发表时间：
2020-08
期刊：
影响因子：
--
通讯作者：
Shangyue Zhu;Alamin Mohammed;A. Striegel
中科院分区：
文献类型：
--
作者：
Shangyue Zhu;Alamin Mohammed;A. Striegel
当存在多个客户端时，使用WiFi网络的视频流带来了可变网络性能的挑战。因此，重要的是要持续监测和预测网络变化，以确保视频流的更高用户体验质量（QoE）。旨在检测这种网络变化的现有方法具有若干缺点。例如，主动探测方法是昂贵的，使得在测试期间产生更多的附加业务流。为了克服其缺点，我们提出了一种被动的，轻量级的方法，CP-DASH，从而利用帧聚合中存在的排队效应来预测WiFi网络中的链路拥塞。这种方法允许早期检测，可以用来适当地调整我们的视频。我们进行实验模拟一个WiFi网络与多个客户端和比较CP-DASH与五个当代速率选择机制。我们发现，我们所提出的方法显着降低开关率和失速率从22%到5%和从38%到25%相比，现有的基于吞吐量的算法，分别。
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.