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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The main challenges with regards to QoE optimization and control may be summarized in the answers to four questions: what key quality parameters to optimize and control; where to control; when to perform QoE optimization and control; and how often to control and optimize QoE.
“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]
Recent efforts exploring QoE management using SDN and/or NFV are limited by a limited review and discussion of standardization activities related to SDN and/or NFV.
“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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A QoE Driven Cross‐Domain Management Architecture for Space‐Air‐Ground Integrated Network
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- 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 · 2016: 5 · 2015: 1
- 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
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.
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