## 5 Enhancing QoS and QoE in ICN
### In-Network Caching
Figure 3 shows a simplified architecture of HCaching, where a large Layer 2 (L2) cache (DRAM) is masked behind a small Layer 1 (L1) cache (SRAM). The SRAM contains three main components: (i) the L2 \(A^{2}\) Index, which uses the \(A^{2}\) buffering algorithm [54] to index L2 cache, (ii) the Reading Cache, managed by the LRU algorithm, and (iii) the Writing Cache, which also utilizes the LRU algorithm. Simulation results showed that HCaching required much smaller SRAM than LRU and DRAM_SSD [55] for all DRAM sizes and OPC [56] for DRAM sizes more than 1 GB. In addition, HCaching achieved a throughput close to 100 Gb/s, LRU 10 Gb/s, DRAM_SSD 15 Gb/s, and OPC less than 10 Gb/s. [PERSON] et al. [57] investigated the financial implications of ICN, specifically evaluating its cost-effectiveness prior to widespread adoption within internet service provider (ISP)infrastructures. The authors introduced a caching strategy informed by cost considerations to analyze the trade-off between enhancing QoS and minimizing the economic overheads. Content popularity, especially for multimedia objects, was studied in [58; 59]. [PERSON] et al. [59] proposed DASCache, a framework that finds optimal video content placement in the transmission queue of the router. Figure 4 illustrates the queueing model for DASCache's adaptive video streaming. In each round, the most popular video chunk is placed in the front of the queue, which minimizes the average access time per bit to all users and thereby increases their respective throughput. [PERSON] et al. [60] and [PERSON] et al. [61] proposed cooperative and distributed caching strategies for CCN. In [60], a heterogeneous cache allocation method distributed the cache capacity across different CRs. The importance of CRs was evaluated based on network topology and traffic characteristics. The proposed model demonstrated an improved cache hit ratio and utilization and lower routing delay. However, this framework had a high computational complexity, and the study was based on a single topology. On the other hand, a fog computing-based model was proposed in [61]. The authors integrated CCN with fog computing to improve content availability and energy efficiency by optimizing content placement within the network. Figure 4: Queueing model for adaptive streaming. The most popular chunks are placed at the front of the queue. Routers maintain separate queues for each outgoing interface (e.g., 1 and 2 in the figure). Figure 3: The HCaching architecture combines SRAM and DRAM to increase the throughput of ICN routers. Another downside of in-network caching is that content producers do not know where their content is cached, which introduces a problem when the producer wants to update or change the content. [PERSON] et al. [62] proposed a content update scheme for CCN that added extra fields to the interest packets to facilitate version records and backtracking. ndnSIM simulations demonstrated reduced network overhead, lower delay, and improved QoE. Table 2 lists various ICN frameworks that utilize new caching and allocation strategies to enhance QoS and QoE metrics, the experimental platform they used, the type of network (if details available), and the QoS or QoE metrics they optimized. Comparative Analysis of Caching-Based Solutions  
Based on the references listed in Table 2, we can categorize the strategies into a few general subgroups: (i) popularity-based, (ii) cooperative, (iii) topology-aware, (iv) allocation-based, (v) energy-efficient, and (vi) hybrid. Popularity-based caching techniques prioritize cache content based on its access frequency. Caching frequently accessed content increases the likelihood of satisfying future requests from the local cache, increasing the cache hit ratio and reducing delay compared to popularity-agnostic methods [72]. However, accurately estimating content popularity (local or global) is a challenge. Local estimation might miss broader trends, while global estimation can introduce communication overhead. In addition, pure popularity-based