A Generic Quantitative Relationship to Assess Interdependency of QoE and QoS
Open accessAbstract
Researchers as well as network providers are increasingly interested in understanding how a network user's experience changes in relation to different quality of service (QoS) parameters, with many attempts being made to identify the general relationship between quality of experience (QoE) and QoS.Several relations in different function forms have been proposed.There are instances when this variety of proposed functions potentially results in one problem having completely dissimilar solutions at the same time.The question then is which function form is the one that can better explain this relationship.In this paper, three major existing quantitative relationships between QoE and QoS are investigated.After conducting a comprehensive comparative analysis between them, a new definition of QoE-QoS dependency appears.It then results in identifying a new mutual relationship between QoE and QoS.The newly proposed relationship improves the deficiency of previous QoE-QoS relationships along with solving the problem of diversity of simultaneous solutions for one problem.
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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 [240]
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 [240]
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- Authors
- Khorsandroo, S., Noor, R. M., & Khorsandroo, S.
- Affiliations
- University of Malaya (MY)
- Corresponding author
- Sayid Khorsandroo
- Venue
- KSII Transactions on Internet and Information Systems
- Published
- 2013-02-26
- Volume
- 7
- Issue
- 2
- Pages
- 327-346
- ISSN
- 1976-7277 (ISSN-L)
- OpenAlex
- W2246409856
- Cited by
- 23
- References
- 25
- Citations by year
- 2022: 1 · 2021: 2 · 2020: 2 · 2019: 2 · 2018: 2 · 2017: 1 · 2016: 2 · 2015: 1 · 2014: 2 · 2013: 7 · 2012: 1
- FWCI
- 4.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- KSII Transactions on Internet and Information Systems (publishedVersion)
- Funders
- Universiti Malaya
- 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
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Keywords
- quality of experience, quality of service, interdependence, quantitative relationship
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