The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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 citing paper
“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]
Citations
Cites
Internet Low Bit Rate Codec (iLBC)
Andersen, S. V., Duric, A., Astrom, H., Hagen, R., Kleijn, W. B., & Linden, J. (2004) · doi:10.17487/rfc3951 · Cited by 102
Perceptual-based Quality Metrics for Image and Video Services: A Survey
Engelke, U., & Zepernick, H. (2007) · Next Generation Internet Networks (NGI) · doi:10.1109/ngi.2007.371215 · Cited by 94
Hoßfeld, T., Hock, D., Tran‐Gia, P., Tutschku, K., & Fiedler, M. (2008) · Cited by 70
Self-citationMorton, A., Ciavattone, L., Ramachandran, G., Shalunov, S., & Perser, J. (2006) · doi:10.17487/rfc4737 · Cited by 67
Perser, J., Morton, A., Ciavattone, L., Ramachandran, G., & Shalunov, S. (2006) · RFC · Cited by 61
Measurement and Analysis of Skype VoIP Traffic in 3G UMTS Systems
Tutschku, K., Hoßfeld, T., Binzenhöfer, A., & Fiedler, M. (2005) · Cited by 30
Self-citation
Research works printed but not linked: 5
- 2005, Estimating End-to-End Performance in IP Networks for Data Applications
- 2003, Mapping Function for Transforming P 862 Raw Result Scores to MOS-LQO
- khirman, 2002, Relationship between Quality-of-service and Quality-of-Experience for Public Internet Service, 3rd Passive Active Measurement Wksp
- bouch, 2000, Quality Is in the Eye of the Beholder Meeting User's Requirements for Internet Quality of Service
- 2008, Vocabulary for Performance and Quality of Service
Cited by
Sorted by citations
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D. T., Wang, P., Liang, Y., & Kim, D. I. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916583 · Cited by 2,181
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
Qualinet White Paper on Definitions of Quality of Experience
Callet, P. L., Möller, S., Perkis, A., Brunnstrom, K. E., Beker, S., Moor, K. D., Dooms, A., Egger, S., Garcia, M.-N., Hoßfeld, T., et al. (2013) · HAL · Cited by 693
Cognitive internet of things: A new paradigm beyond connection
Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., & Long, K. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2311513 · Cited by 550
Quantification of YouTube QoE via Crowdsourcing
Hoßfeld, T., Seufert, M., Hirth, M., Zinner, T., Tran‐Gia, P., & Schatz, R. (2011) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2011.87 · Cited by 338
Self-citation
Metadata
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
