A generic quantitative relationship between quality of experience and quality of service
Open accessAbstract
Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters.
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Some works find it sufficient to correlate QoE to a single network QoS parameter, while others argue that multiple QoS parameters impact QoE and need to be considered in tandem.
“While some find it sufficient to correlate the QoE to a single network QoS parameter [145, 240, 383, 408], e.g.delay or throughput, others argue that multiple QoS parameters impact the QoE and need to be considered in tandem as features in a QoE/QoS correlation model [89,102,114,235,287,328,432].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [145]
In the literature, simple-regression models with a single feature are most dominant, although the collective impact of different QoS parameters was also considered.
“In the literature, simple-regression models with a single feature are most dominant [145, 240, 383, 408], although the collective impact of different QoS parameters was also considered [23,132].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [145]
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Quality of Experience-Aware User Allocation in Edge Computing Systems: A Potential Game
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A survey on QoE-oriented wireless resources scheduling
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- Authors
- Fiedler, M., Hossfeld, T., & Tran-Gia, P.
- Affiliations
- Blekinge Institute of Technology (SE)
- University of Würzburg (DE)
- Venue
- IEEE Network
- Published
- 2010-03
- Volume
- 24
- Issue
- 2
- Pages
- 36-41
- ISSN
- 0890-8044 (ISSN-L)
- OpenAlex
- W2022359415
- Cited by
- 837
- References
- 6
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 23 · 2023: 21 · 2022: 40 · 2021: 36 · 2020: 53 · 2019: 74 · 2018: 65 · 2017: 64 · 2016: 78 · 2015: 86 · 2014: 87 · 2013: 77 · 2012: 58
- FWCI
- 65.69
- Type
- journal article
- Language
- en
- Open access
- green
- urn.kb.se
- Locations
- IEEE Network (publishedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- quality of experience, quality of service, exponential relationship, user perception, streaming services, mean opinion score, packet loss, packet reordering, jitter, web browsing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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