# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 5 Strategies for QoE Measurement in CIAs
### Strategies Based on Screen Updates
The cloud server of a remote desktop or cloud game sends screen updates to the client as fast as possible. The server must adjust its screen refresh rate based on the available resources (e.g., network bandwidth or CPU usage of the computers involved). To avoid overloading clients, some solutions only send screen updates once the client confirms receipt of the previous update. During this time gap, the screen of the cloud server may have changed. Instead of sending individual screen updates for each change, the cloud server unifies all changes into a single screen update. In this way, the CIA merges multiple screen changes occurring at the same pixel location into its most recent pixel value. To quantify this phenomenon, there are three alternatives in the literature. In [39], [PERSON] et al. proposed a method for evaluating cloud gaming. In their study, the authors used external cameras pointed at two monitors: one connected to the thin client and the other to the cloud server. To correctly identify on-screen updates, the proposed methodology inserts a colour bar at the top of the screen that unambiguously encodes each frame. Duringpost-processing of the videos, the authors extracted the time difference between instances when the screens represented the same update. In this case, the authors did not provide an order of magnitude of the time difference that would allow the user to be satisfied with the CIA, and they required the client and the cloud server to be close to each other during the test. Finally, [PERSON] et al. [43] proposed their distortion-over-latency (DOL) metric, seeking to combine interactivity time and rendering quality in cloud gaming CIAs. To obtain the DOL, the authors calculated the distortion between the screen update rendered by the thin client and the ideal screen update before being sent by the cloud server. The DOL is the product of the MSE of these two screen updates and the time elapsed until the thin client receives the distorted frame (synthetic interactivity time). Tools can easily calculate the DOL offline by saving all the screen updates from the client and the cloud server. However, performing this calculation in real time is complicated because the thin client does not have the original screen update to calculate the distortion. [PERSON] et al. proposed utilising a thin client and a cloud server to perform online calculations. The thin client sends the distorted screen update and the synthetic interactivity time to the server along with the original screen update to calculate the DOL. The authors did not provide a relationship between the DOL and QoE values but stated that they needed more subjective testing to better understand this relationship. There are, therefore, some common weaknesses in the proposals based on screen updates. In general, they do not provide an order of magnitude for the obtained metrics to ensure user satisfaction with QoE. Some of the proposals do not allow real-time operation, and others do not take into account possible measurement errors or obstacles in the methodology used. However, this cannot be known in all the proposals we analysed because some do not provide sufficient technical details to replicate their operations. Additionally, some proposals are dependent on a specific system, application, or environment, as they rely on utilising equipment, applications, or drivers.