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.