### Related Work  
The communications ecosystem is constantly growing. It has facilitated new remote business, both in terms of versatile innovation and the entry of new members. That is, new industries that have seen an economic benefit from the possibility of serving millions of devices efficiently. One of the hottest research topics is applying intelligence to the network. Intelligence can be applied mainly in two stages: application level (closer to end-user) or network level (infrastructure). There are several ways to use Artificial Intelligence (AI) at the application level. With regard to the application level, our work relies on image analysis tools and the use of relevant metrics, e.g., Quality of Experience (QoE). QoE, in the video domain, is a subjective measure used in different works [2] as an estimate of perceived quality based on the analysis of image characteristics.  
However, the future of communications calls for a virtualized world based on microservice systems. This underscores the importance of adding smart services also at the network level. In the work [3], a cloud architecture is analyzed on which to deploy a service virtualization platform (SVP) to facilitate the development, deployment and operation of media services on 5G networks. These edge-to-cloud architectures represent the new deployment paradigm in which the different services are assembled and interconnected. The simultaneous application of 5G frameworks with QoE modeling in multimedia services enables works such as [4], where it subjectively compares not only the results obtained in the laboratory, but also the empirical results gathered during the testbed.  
The multimedia adaptation of streaming requires the full participation of an intelligence. That is, a service capable of monitoring and acting accordingly. Certain works such as [5] began these studies with the application of RL to content adaptation. In more recent studies, thanks to 5G tools and QoE estimation, the possibilities of applying RL algorithms to optimize QoE were analyzed. The first of them [6] focuses on reducing buffering times in streaming, while other works advocate an adaptation of the bitrate with different videos scaled in bitrate [7] with the same objective. Both works aim to improve the QoE of users avoiding video stalls in off-line content consumption environments. However, QoE is estimated through other parameters, such as bitrate and buffer size. These scenarios are not meant for live content, but for on-demand content consumption.  
The first major difference with respect to our work relates to the influence that network problems can have on QoE. In this case, QoE is affected by stalls in the video experience. The work estimates the reduction of QoE based on the times when a rebuffering process is needed. On the other hand, we present a work that actually measures the QoE, with the objective of not only guaranteeing the reduction of video artifacts, in our case consisting of blockiness, block loss, and blurring, but to also improve the experience by providing the highest possible QoE.  
The scope of remote content production is one of the keys to analyze in terms of service optimization. Although 5G networks can support high binary rates, there are cases where conditions are not adequately met and service experience is affected. For example, in remote production cases [8], dedicated point-to-point connections are established between the content provider and the central server to try to guarantee the best possible conditions. However, due to network saturation, this point-to-point connection may not be in the most optimal conditions. The result is poor end-user QoE. The proposed idea for solving similar problems is to apply optimization techniques in which, through AI techniques, we can estimate which are the most appropriate corrective actions to maximize QoE for the end-user.