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

Loh, Frank; Poignée, Fabian; Wamser, Florian; Leidinger, Ferdinand; Hoßfeld, Tobias

doi:10.3390/s21124172

Open AccessArticle

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

by

Frank Loh

^*

,

Fabian Poignée

,

Florian Wamser

,

Ferdinand Leidinger

and

Tobias Hoßfeld

Institute of Computer Science, University of Würzburg, 97074 Würzburg, Germany

^*

Author to whom correspondence should be addressed.

Sensors 2021, 21(12), 4172; https://doi.org/10.3390/s21124172

Submission received: 15 April 2021 / Revised: 1 June 2021 / Accepted: 9 June 2021 / Published: 17 June 2021

(This article belongs to the Collection Machine Learning for Multimedia Communications)

Download

Browse Figures

Review Reports Versions Notes

Abstract

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.

Keywords: HTTP adaptive video streaming; quality of experience prediction; machine learning

Share and Cite

MDPI and ACS Style

Loh, F.; Poignée, F.; Wamser, F.; Leidinger, F.; Hoßfeld, T. Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming. Sensors 2021, 21, 4172. https://doi.org/10.3390/s21124172

AMA Style

Loh F, Poignée F, Wamser F, Leidinger F, Hoßfeld T. Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming. Sensors. 2021; 21(12):4172. https://doi.org/10.3390/s21124172

Chicago/Turabian Style

Loh, Frank, Fabian Poignée, Florian Wamser, Ferdinand Leidinger, and Tobias Hoßfeld. 2021. "Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming" Sensors 21, no. 12: 4172. https://doi.org/10.3390/s21124172

APA Style

Loh, F., Poignée, F., Wamser, F., Leidinger, F., & Hoßfeld, T. (2021). Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming. Sensors, 21(12), 4172. https://doi.org/10.3390/s21124172

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI