TY  - JOUR
A1  - Loh, Frank
A1  - Poignée, Fabian
A1  - Wamser, Florian
A1  - Leidinger, Ferdinand
A1  - Hoßfeld, Tobias
T1  - Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming
T2  - Sensors
N2  - 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.
KW  - HTTP adaptive video streaming
KW  - quality of experience prediction
KW  - machine learning
Y1  - 2021
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/24112
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-241121
SN  - 1424-8220
VL  - 21
IS  - 12
ER  -