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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