QoE Management in Wireless Networks [electronic resource] /
By: Wang, Ying [author.].
Contributor(s): Zhou, Wen'an [author.] | Zhang, Ping [author.] | SpringerLink (Online service).
Series: SpringerBriefs in Electrical and Computer Engineering: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XII, 60 p. 26 illus., 12 illus. in color. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319424545.Subject(s): EXTC Engineering
| Communications Engineering, Networks | Computer Communication Networks | Information Systems and Communication ServiceDDC classification: 621.382 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
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Springer Nature eBookSummary: 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.
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.
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