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This SpringerBrief presents research results on QoE management schemes for mobile services, including user services, and resource allocation. Along with a review of the research literature, it offers a data-driven architecture for personalized QoE management in wireless networks. The primary focus is on introducing efficient personalized character extraction mechanisms, e.g., context-aware Bayesian graph model, and cooperative QoE management mechanisms. Moreover, in order to demonstrate in the effectiveness of the QoE model, a QoE measurement platform is described and its collected data examined. The brief concludes with a discussion of future research directions. The example mechanisms and the data-driven architecture provide useful insights into the designs of QoE management, and motivate a new line of thinking for users' satisfaction in future wireless networks.



Chapter 1. Introduction

In recent years, with the advancement of wireless communication networks, there is an increasing demand especially on mobile Internet services. Users’ Quality of Experience (QoE) becomes one of the main issues for future wireless networks when designing personal and customized services to maintain and attract more users. Furthermore, the research on wireless resource management is moving forward from enhancing objective system performance to improving users’ subjective experience. A better QoE-oriented resource allocation policy is preferred and many new challenges are brought out accordingly, including how to quantify and measure QoE, how to design a set of unified wireless resource management strategies and how to make use of a huge amount of available data to derive an optimal QoE model, etc. Therefore, personalized QoE management, efficient estimation, and optimal resource allocation need to be studied and implemented in future wireless networks.
Ying Wang, Wen’an Zhou, Ping Zhang

Chapter 2. Background and Literature Survey

In this chapter, an overview of QoE is given and the current state-of-the-art background for QoE is described. Specifically, the definitions of QoE from different aspects and QoE influencing factors are first presented. Then, QoE assessment methods and QoE models are introduced in the following section. In addition, QoE management and control issues are also investigated. Last but not least the challenges of QoE in 5G wireless networks are discussed.
Ying Wang, Wen’an Zhou, Ping Zhang

Chapter 3. Architecture of Data-Driven Personalized QoE Management

In this chapter, we propose a systematic architecture on data-driven personalized QoE management. A framework of the QoE management architecture is firstly introduced, which consists of two modules namely (1) training module and (2) control module. We also depict two models for the prediction of user preference, including Bayesian Graphic Model and Context Aware Matrix Factorization Model. A preliminary use case is deployed to demonstrate and evaluate the proposed architecture. Simulation results illustrate the superior performance of proposed architecture compared with traditional water-filling method.
Ying Wang, Wen’an Zhou, Ping Zhang

Chapter 4. QoE-Oriented Resource Allocation in Wireless Networks

User-subjective experience and personalized preference play a very important role in QoE. In this chapter, we intend to develop personalized QoE management by considering the user preference. The proposed personalized QoE model integrates objective QoS parameters and subjective user preference to evaluate user QoE and thus a more reasonable QoE assessment is achieved. Then, the personalized QoE model is applied to the sequential resource allocation and a more specialized and refined management scheme can be attained. For both conventional and personalized resource allocation schemes, numerical simulations are conducted to present the effectiveness of the proposed algorithms.
Ying Wang, Wen’an Zhou, Ping Zhang

Chapter 5. Implementation and Demonstration of QoE Measurement Platform

This chapter describes the state-of-the-art of QoE experiment. Then using a streaming media application as an example, how to design subjective experiment is presented in particular to the QoE-related factors and measurement criterion definition. The detailed procedure and infrastructure is then presented and illustrated. Finally, a conclusion is given.
Ying Wang, Wen’an Zhou, Ping Zhang

Chapter 6. Conclusion

In this chapter, we summarize the main innovations and contributions of this book before future work is discussed.
Ying Wang, Wen’an Zhou, Ping Zhang
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