Institute of Computer Science, University of Würzburg, 97074 Würzburg, Germany.
Sensors (Basel). 2021 Jun 17;21(12):4172. doi: 10.3390/s21124172.
Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.
如今，流媒体视频占据了互联网流量的大部分。出于这个原因，互联网服务提供商和网络运营商试图对终端用户的流媒体质量进行预测和评估。当前的监控解决方案基于各种不同的机器学习方法。目前，提供商和运营商面临的挑战是，现有方法需要大量的数据。在这项工作中，使用一个由超过 13000 个使用原生 YouTube 移动应用程序收集的 YouTube 视频流运行的大量数据集，检查了最相关的体验质量指标，即初始播放延迟、视频流质量、视频质量变化和视频缓冲事件。开发了三个机器学习模型，并进行了比较，以便根据上行链路请求信息估计播放行为。主要重点是开发一种使用尽可能少的特征和数据的轻量级方法，同时保持最先进的性能。
