## 1 Introduction
Although proactive caching has been proposed to further reduce the file delivery time under constrained backhaul resources, it is strongly efficient when users have similar request patterns requiring accurate predictions of users' content demands with advanced learning algorithms [14]. In [15], rate-distortion characteristics of videos, video popularity, client's initial delay, and the transmission capacity for the base station and the MEC server were considered for each cache server to store only the best video presentations in an attempt to improve the clients' QoE. To overcome the problems of video content management at mobile edge caching networks coupled with low-latency guaranteed transmissions, this study proposes a new distributed part of media (DPoM) caching approach that dynamically caches the first part of a video on a MEC Access Node (MAN). The edge cache placement problem to objectively maximize cache hit rate subject to storage and power constraints is modeled as an Integer Linear Programming (ILP) problem [16]. It is infeasible to obtain the optimum solution to the ILP for each user request in a time slot in real-time because it requires significant time due to its NP-completeness. Hence, a heuristic approach is designed to maximize the cache hit rate of the MAN servers in the likeness of a multiple knapsack problem [17], having the cache size of the MAN servers in a cluster to be the total capacity of each knapsack, the probability of a file being requested as the profit of the item, and the size of each file as the weight of the item. The main idea of the proposed scheme is to cache all or most of the files at a regionally organized cloud (ROC). At the MEC access node (MAN), the cache storage is duo-partitioned: main cache and transient cache. The main cache stores the first part of the most popular files a content provider is serving at a period. Relative to the total length of a file, the first part of a file for caching can be 25% to 50% of the whole file since most users engage the video for a few minutes before abandoning the streaming session if they lose interest or experience long rebuffering sessions. The remaining parts of an ongoing streaming activity are fetched into the transient cache when a view-time threshold on the playing file is reached. This threshold is placed on a video to restrict the network to only delivering the remaining parts of a playing file once the threshold is reached, to curtail the high number of session abandonments caused by many factors such as longer startup delays, the negative role of unsteady bitrate changes, the negative role of the number of rebufferings, lack of sufficient interest in the content, etc. [18]. In this direction step, the transient cache is not unnecessarily filled with parts of files whose sessions have been abandoned. The fetched files in the transient cache are stored for a while to serve other users with similar requests before the whole cache is cleared during cache replacement to make room for other file parts in future streaming sessions. Cache update frequency policy of the main cache storage depends on users' preferences dynamism. Thus, our scheme does not only allow for more cached content at the MAN servers, but also improves the cache hit ratio, the latency associated with content fetching, and the overall backhaul traffic. The main contributions of this paper are as follows. 1. This study introduces a resource-sufficient regionally organized cloud which supplies and organizes clusters of neighbor MAN cells. 2. This study gives a formulation of the edge network caching problem as an Integer Linear Programming-based optimization problem objectively to maximize the cache hit ratio. 3. This study sets a file view-time threshold on each file to minimize the backhaul traffic incurred when ongoing streaming sessions are abandoned. 4. In this study, a design of heuristic algorithms to optimally place video contents dynamically in the clustered edge servers is computed.