## 1 Introduction  
Smartphone data traffic is projected to exceed PC data traffic in the next few years based on the Visual Networking Index by Cisco [1]. Internet video traffic accounts for the highest chunk of all smartphone traffic, reaching about 78 exabytes per month. The existing cellular networks will be congested; hence, service delivery will be deplorable if the challenges of video traffic are not addressed. Moreover, the current cloud architecture provides an invisible solution to the problem, as the number of connected edge-hosted containers is also estimated to be over 700 million to provide business resilience networking [2]. As a result of the heterogeneity of network devices and the dynamics of the network, video streams must be converted into multiple copies, each with different bitrates, and stored on main cloud servers. This approach incurs huge operational expenditure, in that it necessitates greater storage capacities and processing power for the content housing and transcoding. Hence, there is a pressing demand to optimize current and future networks to facilitate the delivery of a high quality of service (QoS) and high quality of experience (QoE) at well under the operational expenditure. Network capacity expansion can alleviate the problem with congestion due to data surges, but it is not a feasible solution because it requires huge capital investment. Recently, mobile edge computing, also known as the European Telecommunications Standards Institute (ETSI) Multi-Access Edge Computing (MEC), has been proposed as a promising solution and has been applied in many works in the literature [3, 4, 5, 6, 7]. The advent of this technology ushers in new ways of delivering better services to mobile users. The main challenge of the content delivery problem is that multimedia data is in data centers, while consumers are ubiquitous. The goal of every content delivery network (CDN) is to bring content closer to users via the best output performance considerations. Data centers are mostly located far from urban users. To realize a near-seamless QoE, in-network caching at the edge has gained great research momentum. ETSI MEC offers a network setting and cloud-computing abilities at the mobile network edge, empowering application, and content creators to launch new services, such as intelligent video acceleration, with low latency and high bandwidth. The closeness of storage and computing resources to users has been proven to lessen the burden on the core networks [7, 8]. Caching popular and frequently accessed content within the network also reduces the transit service payments to Internet Service Providers (ISP), thereby reducing the total operational cost of the cellular network and achieving high QoE through fast content delivery by avoiding long-distance transmission [9]. MEC servers, however, are limited in two ways: (a) the caches in edge servers have scarcely constrained storage capacities; and (b) wireless network instability conditions lead to users requesting different bitrates of the same video, making the transcoding computationally resource expensive. Further, caching all the multiple versions of the same video at the network edge poses a greater problem with the cache capacity. Hence, collaborative caching and processing have been introduced. The MEC server caches a higher bitrate version of a video which can be later transcoded into multiple lower versions to meet the demand of users. Clustered MEC servers collaborate to assist each other to deliver from their cache or help transcode the desired bitrate [10, 11]. Some caching strategies, including first-in-first-out (FIFO), least-frequently used (LFU), and least-recently used (LRU) are basic, and their implementations are simple. However, their performance on cache hit ratio and latency is poor when considered in mobile edge caching for media content [12]. Other research-based strategies present solutions to gain efficient management of cache storage and short file delivery time for a less traffic communication [13].