# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 5 Strategies for QoE Measurement in CIAs
### Strategies Based on Audovisual Degradation Measures  
Strategies based on audiovisual degradation measures are those that seek to evaluate the deterioration in visual or sound quality experienced by applications or services when they are utilised through a CIA rather than locally. We explain below how researchers quantify this phenomenon. The literature on the evaluation of CIAs addresses different metrics for quantifying audiovisual degradation. One of the simplest is the number of frames per second (FPS) the user receives from a CIA. [PERSON] et al. [39], [PERSON] et al. [50], and [PERSON] et al. [35] used the FPS as a measure of QoE to evaluate cloud gaming services. If FPS decreases, the authors interpreted that QoE also decreases. In their study, [PERSON] et al. demonstrated that the lower the number of frames per second, the lower the game scores for the players of a cloud gaming service. However, the FPS measure quantifies only the amount of information the client receives, not its quality. This is why, during their experiment, they used software to record the screen of the client and the cloud server. They then compared, frame by frame, both video sources to extract a new distortion metric calculated as the average of the mean squared error (MSE) between the server and client screen pixels for each recorded frame. This proposal also did not provide a relationship between the FPS or the MSE of the FPS and the QoE. In the original slow-motion benchmarking work, [PERSON] et al. [49] focused on providing an approximate measure of interactivity time in remote desktop environments. In addition, the authors offered a mathematical expression to quantify the visual quality experienced by the user when consuming video through a thin client. They proposed a term named visual quality (VQ), which takes as a reference the amount of traffic generated when using a slowed-down version of the applications. The cloud server plays a video stream slowly enough so that the thin client can fully process each frame before it receives the next frame. The methodology records the traffic for each frame of the video as a reference traffic load for seamless playback without degradation. Thus, the visual quality is the ratio between the traffic generated by the CIA at a normal FPS rate and its ideal reference obtained by the tool with a single frame on the network. Some of the proposals mentioned above used this expression in their research to complement the information provided by the VD-DUT metric, for instance, [PERSON] et al. [33] and [PERSON] et al. [51]. In [38], [PERSON] et al. used a similar approach for quantifying QoE on remote desk-tops based on video quality. They started with the assumption that unlimited network bandwidth guarantees the best display quality. Thus, visual quality is the ratio between the traffic that CIAs generate in a bandwidth-constrained environment (the real scenario) and the traffic they generate under unrestricted bandwidth conditions. Although these proposals seek to quantify the visual quality experienced by users of CIAs, they did not clarify what value of the VQ metric satisfies users' QoE or propose a methodology to map it to a proper QoE scale. The peak signal-to-noise ratio (PSNR) [89] and structural similarity method (SSIM) [90] are other metrics that allow for the evaluation of the visual quality of CIAs. Authors using the PSNR or SSIM need to compare the quality of the video stream from the cloud server and that of the user's screen. A low PSNR or SSIM value indicates a large difference in quality between the two video sources. However, the PSNR and SSIM, unlike the MSE, not only measure the frame-by-frame quality of the video sources but also reflect the desynchronization between them. Thus, a delay in receiving frames from the cloud server to the client results in a reduction in the PSNR or SSIM. For the PSNR, researchers must calculate the logarithm of the MSE for each pixel in the image. However, the SSIM is calculated using luminance, contrast, and image structure.