A Survey of Emerging Concepts and Challenges for QoE Management of Multimedia Services
Abstract
Quality of Experience (QoE) has received much attention over the past years and has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modelling QoE for a variety of media services. The next logical step is to actively exploit that accumulated knowledge to improve and manage the quality of multimedia services, while at the same time ensuring efficient and cost-effective network operations. Moreover, with many different players involved in the end-to-end service delivery chain, identifying the root causes of QoE impairments and finding effective solutions for meeting the end users’ requirements and expectations in terms of service quality is a challenging and complex problem. In this article, we survey state-of-the-art findings and present emerging concepts and challenges related to managing QoE for networked multimedia services. Going beyond a number of previously published survey articles addressing the topic of QoE management, we address QoE management in the context of ongoing developments, such as the move to softwarized networks, the exploitation of big data analytics and machine learning, and the steady rise of new and immersive services (e.g., augmented and virtual reality). We address the implications of such paradigm shifts in terms of new approaches in QoE modeling and the need for novel QoE monitoring and management infrastructures.
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Surveys [14,15] focused on QoE in multimedia IoT applications, which is determined based on how such applications are consumed by users.
“In surveys such as [14,15], authors have focussed on QoE in multimedia IoT applications, which is determined based on how such applications are consumed by users.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [15]
Existing studies do not provide insights into how network or service providers can utilize the capabilities of SDN and NFV for QoE management to meet end users' quality demands.
“As such, these studies do not provide insights into how network or service providers can utilize the capabilities of SDN and NFV for QoE management (e.g., to achieve automation, programmability, flexibility, scalability and network control) to meet the end users' quality demands.”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [25]
Existing survey papers on QoE management do not explain how QoE can be improved or optimized using SDN and NFV.
“However, the survey papers mentioned above do not explain how QoE can be improved or optimized using SDN and NFV.”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [25]
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Self-citationEEG correlates during video quality perception
Kroupi, E., Hanhart, P., Lee, J. S., et al. (2014) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 27 · see citing works
Video Quality in 5G Networks: Context-Aware QoE Management in the SDN Control Plane
Awobuluyi, O., Nightingale, J., Wang, Q., & Calero, J. M. A. (2015) · doi:10.1109/cit/iucc/dasc/picom.2015.250 · Cited by 27 · see citing works
Unsupervised QoE field study for mobile YouTube video streaming with YoMoApp
Seufert, M., Wehner, N., Wamser, F., et al. (2017) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2017.7965688 · Cited by 27 · see citing works
Towards a causal analysis of Video QoE from Network and Application QoS
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Quality of experience study for multiple sensorial media delivery
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OTT-ISP joint service management: A Customer Lifetime Value based approach
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Neurophysiological experimental facility for Quality of Experience (QoE) assessment
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An SDN QoE-service for dynamically enhancing the performance of OTT applications
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Guest Editorial QoE-Aware Wireless Multimedia Systems
Martini, M. G., Chen, C. W., Chen, Z., Dagiuklas, T., Sun, L., & Zhu, X. (2012) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2012.120801 · Cited by 23 · cited 1× · see citing works
“QoE-driven network management mechanisms have thus focused on the network provider point of view and considered control mechanisms, such as optimized network resource allocation and efficiency (in particular, in wireless systems [75, 86]), admission control, QoE-driven routing, and so on.”
BackgroundCan context monitoring improve QoE? A case study of video flash crowds in the internet of services
Hoßfeld, T., Skorin-Kapov, L., Haddad, Y., Pocta, P., Siris, V. A., Zgank, A., & Melvin, H. (2015) · 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM) · doi:10.1109/inm.2015.7140480 · Cited by 21 · see citing works
Self-citationFrom Prediction to Action: Improving User Experience With Data-Driven Resource Allocation
Bao, Y., Wu, H., & Liu, X. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2680918 · Cited by 21 · see citing works
QoE-Based Network and Application Management
Schatz, R., Fiedler, M., & Skorin‐Kapov, L. (2014) · T-labs series in telecommunication services · doi:10.1007/978-3-319-02681-7_28 · Cited by 20 · see citing works
Self-citationKhan, N. Y., & Martini, M. G. (2016) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-016-0584-6 · Cited by 20 · see citing works
A fixed-point model for QoE-based charging
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EXPLORATION OF QUALITY OF EXPERIENCE OF STEREOSCOPIC IMAGES: BINOCULAR DEPTH
Chen, W. R., Fournier, J., Barkowsky, M., et al. (2012) · Cited by 19 · see citing works
Features of Quality of Experience
Möller, S., Wältermann, M., & Garcia, M.-N. (2014) · T-labs series in telecommunication services · doi:10.1007/978-3-319-02681-7_5 · Cited by 18 · see citing works
Tackling the Sheer Scale of Subjective QoE
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Towards an efficient methodology for evaluation of quality of experience in Augmented Reality
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When the Crowd Challenges the Lab
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Self-citationMultimodal Physiological Quality-of-Experience Assessment of Text-to-Speech Systems
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Bridging the gap between eye tracking and crowdsourcing
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Quality of experience for adaptation in augmented reality
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Estimating quality of experience on stereoscopic images
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Olfactory-enhanced multimedia video clips datasets
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Sharing is Power: Incentives for Information Exchange in Multi-Operator Service Delivery
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Qualia: A multilayer solution for QoE passive monitoring at the user terminal
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Mechanisms for QoE optimisation of video traffic: a review paper \n
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An Educated Guess on QoE in Operational Networks through Large-Scale Measurements
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Study on the accuracy of QoE monitoring for HTTP adaptive video streaming using VNF
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Mechanisms for QoE optimisation of Video Traffic: A review paper
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QoE for telemedicine: challenges and trends
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Impact of test condition selection in adaptive crowdsourcing studies on subjective quality
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Self-citationAnalysis of crowdsourced sampling strategies for HodgeRank with sparse random graphs
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Towards a Framework for Comparing Application-Network Interaction Mechanisms
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Workshop on Tracking Quality of Experience in the Internet
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Do Scale-Design and Training Matter for Video QoE Assessments through Crowdsourcing?
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Self-citationThe Social Multimedia Experience
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A nonlinear quality of experience model for high dynamic spatio-temporal mulsemedia
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One Shot Crowdtesting: Approaching the Extremes of Crowdsourced Subjective Quality Testing
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Self-citationQuantitative comparison of application–network interaction: a case study of adaptive video streaming
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Network quality differentiation: Regional effects, market entrance, and empirical testability
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Self-citation
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QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
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QoE in IoT: a vision, survey and future directions
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Modeling Quality of IoT Experience in Autonomous Vehicles
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Pal, D., Vanijja, V., Arpnikanondt, C., Zhang, X., & Papasratorn, B. (2019) · IEEE Access · doi:10.1109/access.2019.2917061 · Cited by 39 · see citing works
- Authors
- Skorin-Kapov, L., Varela, M., Hoßfeld, T., & Chen, K.-T.
- Affiliations
- University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia
- EXFO, Finland
- University of Duisburg-Essen (DE)
- Institute of Information Science, Academia Sinica (TW)
- ORCID
- Hoßfeld, Tobias
- Venue
- ACM Transactions on Multimedia Computing, Communications, and Applications
- Published
- 2018-04-30
- Volume
- 14
- Issue
- 2s
- Pages
- 1-29
- ISSN
- 1551-6857 (ISSN-L)
- 1551-6865
- OpenAlex
- W2804559716
- Cited by
- 116
- References
- 147
- Citations by year
- 2026: 1 · 2025: 11 · 2024: 13 · 2023: 11 · 2022: 24 · 2021: 25 · 2020: 17 · 2019: 8 · 2018: 6
- FWCI
- 7.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Multimedia Computing Communications and Applications (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (HO 4770/1-2 (DFG OekOnet))
- Croatian Science Foundation (UIP-2014-09-5605)
- 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, QoE management, QoE modeling, QoE monitoring, softwarized networks, big data analytics, machine learning, augmented reality, virtual reality, network operation
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