A1 Journal article (refereed)
Network Capacity Estimators Predicting QoE in HTTP Adaptive Streaming (2022)

Laine, S., & Hakala, I. (2022). Network Capacity Estimators Predicting QoE in HTTP Adaptive Streaming. IEEE Access, 10, 9817-9829. https://doi.org/10.1109/ACCESS.2022.3145185

JYU authors or editors

Publication details

All authors or editors: Laine, Sanna; Hakala, Ismo

Journal or series: IEEE Access

eISSN: 2169-3536

Publication year: 2022

Volume: 10

Pages range: 9817-9829

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/ACCESS.2022.3145185

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/79569


The aim of adaptive HTTP streaming technology is preserving the best possible video streaming quality for viewers in heterogeneous network conditions. This can be achieved by making multiple quality versions of the video available. Switching between versions during playback should be imperceptible and fluent. The decision about quality-level switching is typically based on network capacity estimation and buffer occupancy, which predict the risk of stalling. Since quality-level switching and stalling are directly evident to the user, they are often classified as influence factors of quality of experience (QoE). In this paper, we observe different network capacity estimators and buffer behavior in limited network conditions and study how the estimators predict QoE. The challenges of variable bitrate (VBR)-encoded video are considered. We also propose two new estimators to predict QoE. One compares segment fetch time to segment playback time, while the other explores the difference of throughput and average download rate. As segment duration may influence HTTP adaptive streaming (HAS) playback in unstable conditions, the findings are tested with four segment lengths. Moreover, streaming quality is analyzed in a testbed using two popular web players to reveal possible effects of the players’ features.

Keywords: streaming; video; algorithms; performance (capacity); user experience; alternatives; quality

Free keywords: streaming media; HTTP adaptive streaming; bit rate; quality of experience; player performance; quality of service; throughput; switches; estimation; bandwidth; adaptive algorithm; network capacity estimators

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 2

Last updated on 2022-20-09 at 15:43