A Survey on QoE-oriented Wireless Resources Scheduling <s> C. Active End-user Device / Active User Strategies <s> In this paper a novel framework for extending QoS to QoE in wireless networks is introduced. Instead of viewing QoE as an off-line apriori mapping between users' subjective perspective of their service quality and specific networking metrics, we treat QoE provisioning as a dynamic process that enables users to express their preference with respect to the instantaneous experience of their service performance, at the network's resource management mechanism. Specifically, we exploit network utility maximization (NUM) theory to efficiently correlate QoE and user-application interactions with the QoS-aware resource allocation process, through the dynamic adaptation of users' service-aware utility functions. The realization of the proposed approach in a CDMA cellular network supporting multimedia services is demonstrated and the achieved benefits from both end-users' and operators' point of view are discussed and evaluated. <s> BIB001 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> C. Active End-user Device / Active User Strategies <s> This letter proposes a quality-of-experience (QoE)- aware scheduler that maximizes the average number of satisfied users. QoE is different from quality-of-service (QoS) in that QoE is determined by users'subjective satisfaction while QoS isobjectively measured. Hence, users' satisfaction needs to be incorporated into the scheduler to better understand QoE maximization. In this regard we propose a simple but effective user participation scheme exploiting one-bit feedback that indicates user's satisfaction upon the end of service. Then, user-centric QoE function is derived, which serves our objective function. Assuming that the QoE func- tion can be successfully modeled by a sigmoid function we solve the non-convex sigmoid optimization. We show that, interestingly, adding non-trivial fairness constraints for starvation prevention can convexify the problem, and we obtain the unique optimal solution. By comparing the case without user participation we show that user participation can substantially improve the total QoE, e.g., nearly 100% improvement of the low 10 percentile users and 40% improvement of the low 50 percentile users. <s> BIB002
As mentioned in the Introduction, the final judge of the received service quality still remains the end-user, i.e. humans. Therefore, scheduling strategies that exploit direct inputs of the users have the advantage of knowing their preferences and if they are satisfied with the service. On the other hand, these approaches require that the end-user devices are capable of receiving the necessary user inputs. The strategies described next and summarized in Table III adopt this active user  approach. In BIB001 , the authors propose a QoE provisioning framework that enables users to dynamically and asynchronously express their preference with respect to the instantaneous experience of their service performance. To attain this goal, a Graphical User Interface (GUI) displays and captures the users' options (increase/decrease quality), the feasibility and the consequences of their action (cost). Upon a user's preference declaration, an online dynamic alteration of the utility functions attributes is performed, in order to exploit the NUM theory by allowing the seamless integration of users' subjectivity in the resource allocation process. A QoE-aware scheduling framework is proposed in BIB002 to maximize the average number of satisfied users, where it is assumed that users can feedback a single bit to indicate their satisfaction levels. Since users are in the control loop of the QoE maximization, non-trivial fairness constraints are also added so as to prevent starvation.