A Survey on QoE-oriented Wireless Resources Scheduling <s> D. Discussion <s> In this paper, the quality of experience (QoE) of real-time video streaming over long term evolution (LTE) networks is investigated. Network QoE metrics are proposed in order to capture the overall performance of radio resource management algorithms in terms of video quality perceived by the end users. Thus, real time video streaming over both the uplink (UL) and downlink (DL) directions is investigated, and LTE dynamic resource allocation is taken into account. Metrics corresponding to average, geometric mean, and minimum QoE in the network are measured when max C/I, proportional fair, and max-min radio resource management algorithms are implemented. Monte Carlo simulation results show that proportional fair scheduling maximizes the average network QoE both in the uplink and downlink, and that it also leads to more fairness. <s> BIB001 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> D. Discussion <s> It is well-known that opportunistic schedulers can exploit multi-user diversity and channel variations in the presence of elastic traffic to improve users' QoE. As more and more adaptive streaming traffic appear in the wireless system, it is needed to verify the performance impacts of deploying opportunistic schedulers and other policies. This paper examines the video bit rate and the buffer surplus in various scenarios of mobility under several scheduling policies, and proposes for operators to deploy round-robin policy in the static scenario. In the cases with users' mobility, there are performance trade-offs between the video bit rate and the buffer surplus and scheduling policy is suggested based on the targeted metric. In order to obtain an intermediate results, discriminatory scheduling is also investigated and suggested in this case. <s> BIB002 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> D. Discussion <s> We study HTTP video streaming over a wireless access cell, such as LTE. We derive a closed form expression for the probability of timely delivery of the streamed video packets as a function of the bandwidth allocated to the user. The probability expression uses application, transport, and physical layer parameters relating to the employed video stream rate and to the quality of the communications channel. We validate the analytical results via numerical simulations based on the execution of real video traces and with LTE channel quality index trace modeling. The attained results allow the system designer, scheduler, and manager to calculate the bandwidth level that should be allocated to maintain acceptable system performance, expressed by two quality-of-experience (QoE) metrics, namely the video rate and the probability of timely delivery of video packets. The model provides a rationale and tools for performing admission procedures, bandwidth pricing policies, and cell dimensioning designs. <s> BIB003 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> D. Discussion <s> User-centric service and application management focuses on the quality of experience (QoE) as perceived by the end user. Thereby, the goal is to maximize QoE while ensuring fairness among users, e.g., for resource allocation and scheduling in shared systems. Although the literature suggests to consider consequently QoE fairness, there is currently no accepted definition of QoE fairness. The contribution of this letter is the definition of a generic QoE fairness index $F$ , which has desirable key properties as well as the rationale behind it. By using examples and a measurement study involving multiple users downloading web content over a bottleneck link, we differentiate the proposed index from QoS fairness and the widely used Jain’s fairness index. Based on results, we argue that neither QoS fairness nor Jain’s fairness index meet all of the desirable QoE-relevant Properties, which are met by $F$ . Consequently, the proposed index $F$ may be used to compare QoE fairness across systems and applications, thus serving as a benchmark for QoE management mechanisms and system optimization. <s> BIB004
Based on what was previously described, QoS-QoE mapping strategies combined with utility-based optimization can be regarded as the fundamental tools to perform the shift from QoS-to QoE-oriented scheduling algorithms design. Ideally, one should aim at obtaining mathematical formulas that relate application, transport, and physical layer parameters to subjective quality experienced by the users. With these it would become possible to know the impact of a certain resource allocation on the user QoE. For example, QoE metrics are proposed in BIB001 in order to capture the overall performance of radio resource management algorithms in terms of video quality perceived by the end users when considering video streaming over LTE. The authors measured average, geometric mean, and minimum QoE when scheduling algorithms like MT and PF are adopted. In BIB002 , the QoE of adaptive video streaming is analyzed under several scheduling policies, like RR and MT, where the QoE is based on the mean video bit rate and the mean buffer surplus. The examination of the performance impact of the different scheduling schemes is then used to suggest the best strategy to be adopted in various mobility scenarios. On the other hand, the QoE-based mathematical formulas also allow wireless systems design to be adjusted so as to enhance the quality perceived by the users. For instance, the authors of BIB003 provide a closed-form expression of the probability of timely delivery of video chunks for DASH video streaming over a wireless cell (such as LTE) as a function of the downlink wireless bandwidth level allocated to the user. As a consequence, and given a certain QoE requirement based on the probability of timely delivery and the received video stream quality level, the aforementioned expression allows to infer the number of users that can be accommodated in the wireless access system, as well as it can be used to design admission procedures, bandwidth pricing policies, and cell dimensioning. In BIB004 , a general fairness metric is formulated for shared systems, which satisfies QoE-relevant properties, assuming that estimated QoE values are known. This QoE fairness metric may be adopted when comparing different resource management techniques in terms of their fairness across users and services, although it says nothing about how good the system is and thus needs to be considered together with the achieved (e.g. mean) QoE in system design.