Empirical study based on machine learning approach to assess the QoS/QoE correlation
Abstract
The appearance of new emerging multimedia services have created new challenges for cloud service providers, which have to react quickly to end-users experience and offer a better Quality of Service (QoS). Cloud service providers should use such an intelligent system that can classify, analyze, and adapt to the collected information in an efficient way to satisfy end-users' experience. This paper investigates how different factors contributing the Quality of Experience (QoE), in the context of video streaming delivery over cloud networks. Important parameters which influence the QoE are: network parameters, characteristics of videos, terminal characteristics and types of users' profiles. We describe different methods that are often used to collect QoE datasets in the form of a Mean Opinion Score (MOS). Machine Learning (ML) methods are then used to classify a preliminary QoE dataset collected using these methods. We evaluate six classifiers and determine the most suitable one for the task of QoS/QoE correlation.
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The parameters of Mushtaq et al.'s models are not disclosed, and the significance of selected features, particularly viewer-related ones, is not disclosed, making their usefulness and practicality in real-life deployment questionable.
“The parameters of the models are not disclosed, and neither is the significance of the selected features in particular the viewer-related ones, whose usefulness and practicality in real-life deployment are questionable.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [328]
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 [328]
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Quality of Experience Provisioning in Mobile Cloud Computing
Moron, B., & Glenn, J. (2018)
Network Service Analysis Based on Feature Selection Using Improved Linear Mixed Model
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Towards Improved Model for User Satisfaction Assessment of Multimedia Cloud Services
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QoE Prediction for Multimedia Services
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Service Level Agreement Optimization via Reverse Engineering the QoE/QoS RelationsPreprint
Mousavi, A. S., Kazemitabar, J., & Ebrahimzadeh, A. (2022) · Research Square · doi:10.21203/rs.3.rs-1929739/v1
Discerning MOS of Video Conferencing via Deep Packet Inspection and Video Context Clues
Qian, C., Huang, Y., Shi, T., Li, J., Xu, Q., Wu, K., Wang, J., & Liu, B. (2025) · International Workshop on Quality of Service · doi:10.1109/iwqos65803.2025.11143511
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- Authors
- Mushtaq, M. S., Augustin, B., & Mellouk, A.
- Affiliations
- Université Paris-Est Créteil (FR)
- Venue
- 2012 17th European Conference on Networks and Optical Communications
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 1-7
- OpenAlex
- W2086253016
- Cited by
- 100
- References
- 18
- Citations by year
- 2026: 1 · 2025: 3 · 2023: 3 · 2022: 4 · 2021: 5 · 2020: 10 · 2019: 10 · 2018: 15 · 2017: 17 · 2016: 7 · 2015: 13 · 2014: 7 · 2013: 5
- FWCI
- 5.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- European Conference on Networks and Optical Communications (publishedVersion)
- HAL (submittedVersion)
- 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 › Sociology and Political Science › Multimedia Communication and Technology
- Keywords
- quality of experience, quality of service, video streaming, cloud networks, mean opinion score, classifier comparison, user experience, network parameters, terminal characteristics
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