This Springer Brief presents research results on Qo E 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 Qo E management in wireless networks. The primary focus is on introducing efficient personalized character extraction mechanisms, e.g., context-aware Bayesian graph model, and cooperative Qo E management mechanisms. Moreover, in order to demonstrate in the effectiveness of the Qo E model, a Qo E 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 Qo E management, and motivate a new line of thinking for users’ satisfaction in future wireless networks.

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