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.