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A Data-Driven Architecture for Personalized QoE Management in 5G Wireless Networks
Abstract
With the emergence of a variety of new wireless network types, business types, and QoS in a more autonomic, diverse, and interactive manner, it is envisioned that a new era of personalized services has arrived, which emphasizes users' requirements and service experiences. As a result, users' QoE will become one of the key features in 5G/future networks. In this article, we first review the state of the art of QoE research from several perspectives, including definition, influencing factors, assessment methods, QoE models, and control methods. Then a data-driven architecture for enhancing personalized QoE is proposed for 5G networks. Under this architecture, we specifically propose a two-step QoE modeling approach to capture the strength of the relationship between users and services. Thereafter, the preferences of a user is introduced to model the user's subjectivity toward a specific service. With the comprehension of users' preferences, radio resources can be distributed more precisely. Simulation results show that overall QoE can be enhanced by 20 percent, while 96 percent of users have an improved QoE, which validates the efficiency of the proposed architecture.
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“As stated in [8], the main challenges that arise with regards to QoE optimization and control may be summarized in the answers to the following four questions: (1) What key quality parameters to optimize and control? (2) Where to control (e.g., at the client, server and the network side)? (3) When to perform QoE optimization and control (e.g., during the service, that is, on-line control or in an”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [19]
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“Although recent efforts explore the QoE management aspects using SDN and/or NFV, we note that these works are limited in at least one of the following: 1) limited review and discussion of the standardization activities related to SDN and/or NFV;”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [19]
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RetractedSkaka-Čekić, F., Husić, J. B., Odžak, A., Hadžialić, M., Huremović, A., & Šehić, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-13803-z · Cited by 1
Hybrid Non-Intrusive QoE Assessment of VoIP Calls Based on an Ensemble Learning Model
Zabetian, N., Azar, G. A., & Khalaj, B. H. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3325290 · Cited by 1
QoEUP: A Preference-Based QoE Optimization Scheme Using Human Feedback for Mobile Video Streaming
Yin, H., Zhang, H., Zhu, J., Yang, S., Wang, J., Chen, P., & Ma, N. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3651560 · Cited by 1
Architecture of Data-Driven Personalized QoE Management
Wang, Y., Zhou, W., & Zhang, P. (2016) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-319-42454-5_3 · Cited by 1
Self-citationHusić, J. B., Baraković, S., Kovac, S., Delija, A., & Kovac, E. (2017) · International Conference on Information, Communication and Automation Technologies (ICAT) · doi:10.1109/icat.2017.8171639 · Cited by 1
Valencia, C., & Camilo, J. (2021) · Cited by 1
Machine Learning in 5G Multimedia Communications
Milovanovic, D. A., Bojkovic, Z. S., & Kukolj, D. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-7708-0.ch010 · Cited by 1
Zabetian, N., & Khalaj, B. H. (2023) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4837 · Cited by 1
Learning to Solve Decision Problems Over Two Timescales: An Application to 5G Puncturing
Randall, M., Belcredi, G., Belzarena, P., & Larroca, F. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10735-3 · Cited by 1
E2E-QoE based 6G Sustainability: Challenges and Designing Aspects
Ji, L., Qian, J., & Wang, H. (2023) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2023-fall60731.2023.10333641 · Cited by 1
Cost-Aware Deep Reinforcement Learning for eMBB-URLLC Multiplexing Resource Allocation
Lin, Z., Shi, N., Zhou, Y., & Shi, J. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978322 · Cited by 1
Metadata
- Authors
- Wang, Y., Li, P., Jiao, L., Su, Z., Cheng, N., Shen, X. S., & Zhang, P.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- University of Agder (NO)
- Shanghai University (CN)
- University of Waterloo (CA)
- Venue
- IEEE Wireless Communications
- Published
- 2017-02
- Volume
- 24
- Issue
- 1
- Pages
- 102-110
- ISSN
- 1536-1284 (ISSN-L)
- OpenAlex
- W2556076181
- Cited by
- 125
- References
- 16
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 5 · 2023: 10 · 2022: 21 · 2021: 16 · 2020: 18 · 2019: 17 · 2018: 17 · 2017: 6
- FWCI
- 10.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Wireless Communications (publishedVersion)
- Funders
- National Science Foundation
- National Natural Science Foundation of China
- Beijing University of Posts and Telecommunications
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- quality of experience, 5G, data-driven architecture, user preferences, QoE modeling, radio resource allocation
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