Article
Uplink vs. Downlink: Machine Learning-Based Quality
Prediction for HTTP Adaptive Video Streaming
Frank Loh * , Fabian Poignée , Florian Wamser , Ferdinand Leidinger and Tobias Hoßfeld
Citation: 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
Academic Editor: Nikolaos Thomos
Received: 15 April 2021
Accepted: 9 June 2021
Published: 17 June 2021
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Institute of Computer Science, University of Würzburg, 97074 Würzburg, Germany;
fabian.poignee@informatik.uni-wuerzburg.de (F.P.); florian.wamser@informatik.uni-wuerzburg.de (F.W.);
ferdinand.leidinger@informatik.uni-wuerzburg.de (F.L.); tobias.hossfeld@informatik.uni-wuerzburg.de (T.H.)
* Correspondence: frank.loh@informatik.uni-wuerzburg.de
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 stream-
ing 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
1. Introduction
The ongoing trend in social life to often use a virtual environment is accelerated
by the COVID-19 pandemic. Throughout the last year in particular, work, social, and
leisure behaviors have changed rapidly towards the digital world. This development finds
resonance in the May 2020 Sandvine report, which revealed that global Internet traffic was
dominated by video, gaming, and social usage in particular, with these accounting for
more than 80 % of the total traffic [1], with YouTube hosting over 15 % of these volumes.
For video streaming, the Quality of Experience (QoE) is the most significant metric
for capturing the perceived quality for the end user. The initial playback delay, streaming
quality, quality changes, and video rebuffering events are the most important influencing
factors [
2
–
4
]. Due to the increasing demand, streaming platforms like YouTube and Netflix
have had to throttle the streaming quality in Europe in order to enable adequate quality for
everybody on the Internet [
5
]. This affects the overall streaming QoE for all end users, and
ultimately the streaming provider’s revenue from long-term user churn.
From the perspective of an Internet Service Provider (ISP), responsible for network
monitoring, the goal is to satisfy their customers and operate economically. Intelligent
and predictive service and network management is becoming more important to guar-
antee good streaming quality and meet user demands. However, since most of the data
traffic is encrypted these days, in-depth monitoring with deep packet inspection is no
longer possible for an ISP to determine crucial streaming related quality parameters. It is
therefore necessary to predict quality by other flow monitoring and prediction techniques.
Furthermore, the increasing load and different volumes of flows, and consequently the
processing power required to monitor each flow, make detailed prediction even more
complex, especially for a centralized monitoring entity.
Sensors 2021, 21, 4172. https://doi.org/10.3390/s21124172 https://www.mdpi.com/journal/sensors
