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Modelling user experience of adaptive streaming video over fixed capacity links
Kristianstad University, Faculty of Natural Sciences, Avdelningen för datavetenskap. Kristianstad University, Faculty of Natural Sciences, Research environment of Computer science (RECS).ORCID iD: 0000-0003-3924-8348
Australien.
Australien.
2021 (English)In: Performance evaluation (Print), ISSN 0166-5316, E-ISSN 1872-745X, Vol. 148, p. 1-12, article id 102199Article in journal (Refereed) Epub ahead of print
Abstract [en]

Streaming video continues to experience unprecedented growth. This underscores the need to identify user-centric performance measures and models that will allow operators to satisfy requirements for cost-effective network dimensioning delivered with an acceptable level of user experience. This paper presents an analysis of two novel metrics in the context of fixed capacity links: (i) the average proportion of a video’s playing time during which the quality is reduced and (ii) the average proportion of videos which experience reduced quality at least once during their playing time, based on an M/M/∞ system. Our analysis is shown to hold for the more general M/G/∞ system for metric (i), but not for (ii) and simulation studies show an unexpected form of sensitivity of metric (ii) to the flow duration distribution, contrary to the norm of increasing variance causing worse performance. At typical operational loads these new metrics provide a more sensitive and information rich guide for understanding how user experience degrades, than the widely used average throughput metric does. We further show that only the combined use of this existing and our new metrics can provide a holistic perspective on overall user performance.

Place, publisher, year, edition, pages
2021. Vol. 148, p. 1-12, article id 102199
Keywords [en]
Adaptive streaming video, User QoE metrics, Video quality metrics, Proportion of time with reduced video quality, Proportion of videos with reduced quality
National Category
Communication Systems Telecommunications Computer Sciences
Identifiers
URN: urn:nbn:se:hkr:diva-21755DOI: 10.1016/j.peva.2021.102199ISI: 000648542900003OAI: oai:DiVA.org:hkr-21755DiVA, id: diva2:1541329
Available from: 2021-03-31 Created: 2021-03-31 Last updated: 2021-05-28

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fulltext(618 kB)205 downloads
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Publisher's full texthttps://www.sciencedirect.com/science/article/pii/S016653162100016X?via%3Dihub

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Arvidsson, Åke

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Avdelningen för datavetenskapResearch environment of Computer science (RECS)
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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