# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 8 Conclusions  
This survey focused on analysing methodologies and tools for the evaluation of QoE in interactive cloud-based applications. We analysed more than 28 proposals from academia and industry and identified the three most relevant human perception aspects that influence the user's QoE: visual quality, sound quality, and interactivity. We also identified the three categories of CIAs--remote desktops, cloud gaming services, and interactive web applications--and the importance of the human perception aspects in each of them. We dissected the functioning schemes of the CIAs and the particularities of each type. We also identified the components that make up the CIA architecture, how they work, and how they differ from other applications deployed in the non-interactive cloud.  
Likewise, we classified the proposals in the literature based on their working strategies: screen updates, slow-motion benchmarking, measures of audiovisual degradation, instrumentation of the programming code, and indirect measures. In this process, we outlined the procedure each proposal uses to quantify QoE and identified its shortcomings. From this, we systematised the identification of the input metrics the proposals extracted from the infrastructure. We identified four main groups of input metrics and more than 21 input metrics common in the state of the art. The taxonomy revealed the tendency of researchers to opt for more than one input metric simultaneously. We further classified the proposals based on the processing they applied to the input metrics. We detected three types of processing: ad hoc, image processing, and artificial intelligence. This taxonomy revealed that artificial intelligence techniques are becoming increasingly frequent, and their results seem promising.  
We grouped the output metrics into five main categories: video metrics, audio metrics, time metrics, other QoS metrics, and QoE metrics. This taxonomy revealed that a large number of proposals focused their studies on providing QoS metrics without clarifying their relationship with QoE and that much work remains to be done in this regard.  
Finally, we dedicated a specific section to open issues, where we discussed possible areas for improvement in the measurement of QoE in CIAs. In particular, it is necessary to deploy strategies that can be generalised to more than one CIA and to define metrics and procedures that allow the research community to compare the accuracy and performance of the different strategies.  
Conceptualisation, J.A.-U. and E.M.; Data curation, J.A.-U. and E.M.; Formal analysis, J.A.-U. and E.M.; Funding acquisition, E.M., D.M. and M.I.; Investigation, J.A.-U. and E.M.; Methodology, J.A.-U., E.M., D.M. and M.I.; Project administration, E.M.; Resources, E.M.; Software, J.A.-U.; Supervision, E.M.; Validation, E.M., D.M. and M.I.; Visualisation, J.A.-U. and E.M.; Writing--original draft, J.A.-U. and E.M.; Writing--review and editing, J.A.-U., E.M., D.M. and M.I. All authors have read and agreed to the published version of the manuscript.  
Funding: This work was supported by the Spanish State Research Agency project number PID2019-104451 RB-C22/AEI/10.13039/501100011033.