## 2 Related Works  
Recent video presentations with increasing user requests and the advancement of mobile technology have paved the way for new video applications and services. However, the resultant data traffic and network congestions define the limitations of current networks in massive data processing and handling. Mobile Edge Computing (MEC), particularly Edge caching, has been proposed to minimize network congestion and ensure low latency communication [19]. MEC is an approach to deploy micro cloud services at the edge of the network by offering storage and computation capabilities [20]. With the increasing demand for video streaming, several solutions have been proposed to cache the data at the network level closest to users [21; 22; 23; 24]. However, MEC storage and computation resources are limited, as large-scale global deployment will seek a fair distribution of resources to output good overall system performance, meaning the resources should be utilized efficiently. Data offloading (caching) and task offloading (transcoding) have been the main hurdle addressed by the existing literature. In [25], [PERSON] et al. designed a RAN-aware adaptive video caching scheme that utilizes radio network information to select appropriate bit-rates for video caching considering video popularity distribution and estimated video request bit-rates from cached videos in a collaborative and replication-avoidable MEC network. [PERSON] et al. [11] proposed a joint collaborative caching and processing framework that supports adaptive bitrate video streaming. In [26], [PERSON] et al. proposed a utility-driven in-network caching where contents are associated with a utility function corresponding to content's related hit probability in order to maximize the profits of caching. To ensure the effectiveness of the massively distributed but small-sized RAN caches, [PERSON] et al. in [27] introduced RAN-aware reactive and proactive caching policies that utilize User Preference Profiles (UPPs) of all active users in a cell. Similarly, to offset the disadvantages of limited cache storage at the network edge, several learning approaches, incorporating content popularity prediction based on user preferences, clustering users based on similar content interests, and optimizing cache placement and replacement techniques have been proposed [7; 28; 29; 30]. A combination of proactive prediction and replacement collaboration (PCR) among MECs to effectively manage the cache storage is proposed in [31]. To achieve high cache hit ratios, the authors in [32] proposed layered video caching for multiple social groups formed by mobile users based on their requests. A Stackelberg game model was developed to study the collaboration among multiple social groups and the cache node as users (players) compete with each other for the number of layers they request to cache. In [33], the authors proposed a lightweight, agile caching with a PID controller to efficiently control the rate for streaming high-quality data. The proposed algorithm minimizes the operations at the edge nodes to avoid overloading the highly constrained edge nodes. In [34], cooperative caching among MEC servers was considered and the video cache hit ratio was improved by caching multiple presentations of videos and transcoding the videos in real-time. The downside of this approach is that caching multiple versions of complete videos reduces the efficient utilization of the MEC cache space. To further improve the efficiency of the cache management, a Multi-Agent Reinforcement Learning (MARL)-based cooperative content caching policy has been proposed in [35], exploiting only the historical content demands of users when users' preferences are unknown. Most of the aforementioned proposals either end up overburdening the edge servers with transcoding and machine learning tasks or use up cache storage quickly.