# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 5 Strategies for QoE Measurement in CIAs  
Analysing the proposals in the state of the art, this survey identifies five main strategies for measuring QoE in CIAs: based on screen updates, slow-motion benchmarking, audiovisual degradation measures, the instrumentation of the programming code, and indirect measures. In the following subsections, we explain what each of the five identified strategies consists of, and we explain the particularities of the works in the literature that use them. Table 3 summarises the revised proposals and strategies. Some works combine  
Figure 4: Details of each component of the CIA’s architecture.  
several strategies in their proposals, but the one considered the main one is identified. For each strategy, we highlight the fundamental procedure and what measures it contributes as a representative metric of QoE.  
For the mapping of metrics in a QoE indicator, the literature traditionally opts for the absolute category rating (ACR) [84]. The ACR scale includes scores of \"bad\", \"poor\", \"fair\", \"good\" and \"excellent\", applicable to the quality of products or services of any kind. However, there is diversity among the different opinions provided by users [85], which makes it difficult to construct this scale. Each individual who consumes a service or application has different expectations because users are used to a specific environment and may be more or less used to the service depending on previous hours of use. This fact causes researchers to opt for solutions that provide more reliable values than those provided by the ACR scale. To do this, the researchers use averaging techniques to eliminate the noise from influential factors in each user's perception [86]. Averaging user feedback allows us to extract the influential aspects from the set of opinions. The tools use numerical scales to transform qualitative opinions into quantitative ones.  
Another classic way of measuring QoE within the realm of information and communication technologies is the mean opinion score (MOS) [87], standardised by the ITU. The MOS consists of averaging the subjective evaluations of the users under study under the same service conditions. Currently, the MOS is the standard QoE evaluation measure [88].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & & & \\ \hline [PERSON] et al. (2003) [49] & & Yes & Yes & \\ \hline [PERSON] et al. (2011) [43] & Yes & & Yes & \\ \hline [PERSON] et al. (2014) [31] & Yes & & & Yes \\ \hline [PERSON] et al. (2015) [51] & & Yes & & \\ \hline [PERSON] et al. (2017) [46] & & Yes & & \\ \hline [PERSON] et al. (2018) [52] & Yes & & & \\ \hline [PERSON] et al. (2018) [38] & & & Yes & \\ \hline [PERSON] et al. (2018) [44] & & & Yes & \\ \hline [PERSON] et al. (2018) [53] & & & Yes & \\ \hline [PERSON] et al. (2019) [42] & & & Yes & \\ \hline [PERSON] et al. (2019) [40] & & & Yes & \\ \hline [PERSON] (2019) [57] & Yes & & & \\ \hline [PERSON] et al. (2019) [33] & & Yes & Yes & \\ \hline [PERSON] et al. (2019) [54] & & & & Yes \\ \hline [PERSON] et al. (2019) [41] & & & & Yes \\ \hline [PERSON] et al. (2020) [36] & & & Yes & \\ \hline Nvidia (2020) [58] & & & & Yes \\ \hline [PERSON] et al. (2020) [35] & & & Yes & Yes \\ \hline [PERSON] et al. (2020) [47] & & & & Yes \\ \hline Leo Bodnar Electronics (2021) [55] & Yes & & & \\ \hline [PERSON] et al. (2021) [39] & Yes & Yes & & \\ \hline [PERSON] et al. (2021) [50] & & Yes & & \\ \hline [PERSON] et al. (2021) [48] & & & & Yes \\ \hline [PERSON] et al. (2021) [34] & Yes & & & \\ \hline [PERSON] et al. (2021) [9] & & Yes & & \\ \hline [PERSON] et al. in [37] & & & Yes & \\ \hline Exoprise (2024) [45] & Yes & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: QoE quantification proposals grouped by strategy.