## 2 Related Work  
Extensive work has been pursued in the field of ICN to improve the overall efficiency of network architecture. In [43; 44; 45; 46; 47], the researchers studied and investigated many cache strategies. Each cache strategy is discussed and evaluated in terms of a cache hit ratio (CHR), cache diversity (CD), and content redundancy (CR). The study in [44] presents a content placement strategy. The effectiveness of the proposed strategy is evaluated in terms of CHR, CD, and CR. The radio content router (RCR) caches the content according to between centrality router strategy, i.e., the router with most connections to other routers. The strategy is also evaluated in terms of CHR, CD, CR, and stretch based on the content placement. Similarly, in [45] authors have discussed and evaluated different probabilistic cache strategies in terms of the same parameters of CHR, CR, CD, and stretch. Furthermore, the authors presented a 5G-based ICN infrastructure content caching at BS, and user [37]. The authors proposed a joint optimizing the caching scheme, which discusses transcoding and routing decisions for adaptive video streaming over ICN [48]. They evaluated the work in terms of access delay and CHR. Most ICN work pursued primarily considers the model based on content providers, content caching, and content delivery. In [49] the authors have dealt with these three areas. The work mainly focuses on the content provider's selection, caching, and content delivery. The authors have proposed a cache-aware QoS routing scheme. This routing scheme discusses the proximal content available to the consumer. They have also devised the routing decision based on the appropriate FIB of the nearest proximal data providers. In [50] the Authors have investigated a new cache strategy by implementing the unsupervised learning-based proactive caching scheme for ICN networks. They introduced proactive data caching while creating clusters of the BS. Moreover, they evaluated their caching scheme based on CHR and user satisfaction ratio. In [10] authors have discussed the communication perspective regarding the VR users only keeping in view the IP protocol for communication. They worked in providing better QoS for haptic-driven VR users only. Moreover, our work improves the QoS by implementing the ISO and reduces the delay for better QoS for both AR and VR users. In the perspective of edge network caching, ML method enables edge devices to monitor the network environment actively and intelligently by learning and predicting various network characteristics such as channel dynamics, traffic patterns, and content requests, allowing them to take proactive decisions that maximize a predefined objective such as QoS and CHR [51]. Currently approximately all research challenges are utilizing AI to obtain optimal results and performance. Similarly AI is also widely used for edge caching most of the current approaches use more than one machine learning methodology [52]. Unsupervised methods mainly clusters the users, contents, and BSs based on their location or access parameters [53; 54]. Unsupervised learning approaches investigates structure or patterns in unlabeled data, such as user geolocation or content requests patterns. Given the network states, actions, and cost, RL algorithms are used to address the core problems of cache optimization [55]. The majority of published work deals with caching and main goal is to increase CHR [56]. CHR variations are intimately linked to all other caching targets [57]. When content is available in-network and not accessed via CDNs and backhaul lines, it directly affects content access latency, user QoE and,data offload ratio [58]. Therefore almost no work is done towards intelligent and efficient routing scheme for ICN infrastructure. The caching strategies proposed so far are introduced for efficient content placement according to user's request pattern. Different network topologies are used to verify the caching strategies. Each caching strategy provides different stretch results for requested content.