A Survey on QoE-oriented Wireless Resources Scheduling <s> B. QoS-QoE Mapping Strategies <s> Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters. <s> BIB001 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> B. QoS-QoE Mapping Strategies <s> The typical procedure for evaluating the performance of different objective quality metrics and indices involves comparisons between subjective quality ratings and the quality indices obtained using the objective metrics in question on the known video sequences. Several correlation indicators can be employed to assess how well the subjective ratings can be predicted from the objective values. In this paper, we give an overview of the potential sources for uncertainties and inaccuracies in such studies, related both to the method of comparison, possible inaccuracies in the subjective data, as well as processing of subjective data. We also suggest some general guidelines for researchers to make comparison studies of objective video quality metrics more reliable and useful for the practitioners in the field. <s> BIB002 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> B. QoS-QoE Mapping Strategies <s> The LTE specification provides Quality of Service (QoS) of multimedia services with fast connectivity, high mobility, and security. However, 3GPP specifications have not defined scheduling algorithms to support real-time and non-realtime application services, this has led to the development of a variety of scheduling algorithms in the recent years. Performance is a key factor for assessing the scheduling algorithms. However, the performance analysis and evaluation for existing scheduling algorithms are limited to QoS parameters (e.g. in terms of throughput, packet loss and delay) and/or fairness metrics. It is not clear how these scheduling algorithms perform in terms of Quality of Experience (QoE) metrics (e.g. MOS score) which are more closely linked with end user experience or user perceived quality. In this paper, we investigate and evaluate the performance of three popular LTE downlink schedulers (proportional fairness (PF), exponential proportional fairness (EXP-PF), and modified largest weight delay first (M-LWDF)) in terms of QoE metric (i.e. MOS score) in addition to normal QoS metrics (e.g. packet loss and delay) for VoIP applications. Simulation results (based on LTE-Sim) show that the best suitable downlink scheduling algorithm is MLWDF, which has short end-to end delay (less than 50ms) and allowing maximum number of user access (more than 50 users) at acceptable MOS score (MOS over 3.5). The widely used scheduling algorithm (PF) will increase its end-to-end delay over 200ms, when number of user access is over 30. This will limit its application for VoIP when user access number increases. This work would help to assist the design and development of QoE-driven scheduling algorithms for mobile multimedia applications in the future. <s> BIB003 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> B. QoS-QoE Mapping Strategies <s> This paper presents a brief review of some existing correlation models which attempt to map Quality of Service (QoS) to Quality of Experience (QoE) for multimedia services. The term QoS refers to deterministic network behaviour, so that data can be transported with a minimum of packet loss, delay and maximum bandwidth. QoE is a subjective measure that involves human dimensions; it ties together user perception, expectations, and experience of the application and network performance. The Holy Grail of subjective measurement is to predict it from the objective measurements; in other words predict QoE from a given set of QoS parameters or vice versa. Whilst there are many quality models for multimedia, most of them are only partial solutions to predicting QoE from a given QoS. This contribution analyses a number of previous attempts and optimisation techniquesthat can reliably compute the weighting coefficients for the QoS/QoE mapping. <s> BIB004 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> B. QoS-QoE Mapping Strategies <s> Quality of experience (QoE) measures the overall perceived quality of mobile video delivery from subjective user experience and objective system performance. Current QoE computing models have two main limitations: ::: ::: 1) insufficient consideration of the factors influencing QoE, and; ::: ::: 2) limited studies on QoE models for acceptability prediction. ::: ::: In this paper, a set of novel acceptability-based QoE models, denoted as A-QoE, is proposed based on the results of comprehensive user studies on subjective quality acceptance assessments. The models are able to predict users’ acceptability and pleasantness in various mobile video usage scenarios. Statistical regression analysis has been used to build the models with a group of influencing factors as independent predictors, including encoding parameters and bitrate, video content characteristics, and mobile device display resolution. The performance of the proposed A-QoE models has been compared with three well-known objective Video Quality Assessment metrics: PSNR, SSIM and VQM. The proposed A-QoE models have high prediction accuracy and usage flexibility. Future user-centred mobile video delivery systems can benefit from applying the proposed QoE-based management to optimize video coding and quality delivery decisions. <s> BIB005
QoS-QoE mapping strategies have been proposed to quantify QoE, thus making a transition from QoS-to QoE-oriented optimization. QoS-QoE mapping relies on various QoS parameters, which can be divided into two levels: network QoS parameters, such as packet loss rate or delay, and application QoS parameters, like rebuffering events or buffer level. Therefore, QoS-QoE mapping strategies try to find out the relationship between QoE and the two QoS levels, where the network QoS parameters are sometimes first mapped into application QoS parameters -cf. Fig. 3 . Nevertheless, both types of QoS parameters can always be regarded as objective quality metrics, since their measurement is always well defined as they do not depend on any subjective judgment. Choosing a function that maps objective quality metrics into subjective score is not a straightforward task. If both objective and subjective scales were uniform, i.e., if equal perceived quality difference corresponded to equal numerical difference over the whole range, then a linear mapping function could be used BIB002 : where the parameters a e b are obtained by applying linear fit between objective quality metrics M and the respective measured subjective scores. However, the practical objective quality scores are rarely scaled uniformly, hence linear mapping may give too pessimistic view of the performance. To overcome this issue, nonlinear mapping functions have been adopted, discussed and compared BIB002 , BIB004 . The most widely used nonlinear mapping functions are summarized as follows: • Cubic polynomial BIB002 ,  - [26] : • Logistic functions BIB002 ,  , BIB005 : • Exponential function BIB002 , BIB001 : • Power function BIB002 : • Logarithmic function  : In all cases, after computing the predicted QoE values, correlation analysis should be performed between these and actual scores, in order to evaluate the goodness of the mapping strategy. On the other hand, traditional QoS optimization techniques can be applied and assessed in QoE optimization scenarios by making use of QoS-QoE mapping strategies. For instance, in BIB003 , the authors investigate and evaluate the performance of three downlink schedulers (PF, M-LWDF and EXP/PF) in terms of QoE metric for VoIP applications over LTE, making it possible to choose the most suitable one in terms of subjective experience.