Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming

• Main Authors: Frank Loh, Fabian Poignée, Florian Wamser, Ferdinand Leidinger, Tobias Hoßfeld
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Sensors
• Subjects: HTTP adaptive video streaming; quality of experience prediction; machine learning
• Online Access: https://www.mdpi.com/1424-8220/21/12/4172

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.