## 5 Enhancing QoS and QoE in ICN
### Transmission and Flow Control  
Transmission and flow control mechanisms play fundamental roles in enabling efficient and reliable content delivery. Transmission in ICNs involves the dissemination of content packets from producers to consumers across the network. To ensure efficient transmission, ICNs often employ caching strategies at intermediary nodes to store frequently accessed content, reducing the need for long-distance transmissions and improving content \begin{table}
\begin{tabular}{l l l l l} \hline \hline \# & **Proposal and** & **Architecture** & **Experimental** & **Optimized QoS/QoE Metric** \\  & **publication Year** & & **Environment** & \\ \hline
1. & [PERSON] et al. [94], 2013 & CCN & CCNx, Pronto 3290 & Throughput \\  & & & OpenFlow switch & \\ \hline
2. & [PERSON] and [PERSON] [91], 2016 & ICN & Simulation (platform details not included), wireless network & Statistical QoS, power consumption \\ \hline
3. & [PERSON] and [PERSON] [95], 2016 & ICN & Mininet [98] & QoE, delay, throughput \\ \hline
4. & [PERSON] et al. [97], 2019 & ICN & Mininet, wired network & Throughput, QoE \\ \hline
5. & [PERSON] et al. [96], 2021 & NDN & Mininet & Download time (delay) \\ \hline
6. & [PERSON] et al. [92], 2023, & NDN & Mininet, mobile network & Delay, packet loss, jitter, throughput \\ \hline
7. & [PERSON] et al. [99], 2024 & ICN & Python programming & Cache hit ratio, cost, power consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SDN-based strategies: architectures, experimental platforms, and QoS/QoE metrics. delivery latency. Additionally, transmission in ICNs may involve multicast or anycast mechanisms to efficiently transmit content to multiple consumers or replicate content across distributed nodes, enhancing scalability and resilience. Flow control mechanisms in ICNs regulate the rate of content transmission to prevent congestion and ensure fair resource utilization. This is particularly important in scenarios where multiple consumers are competing for the same content or where network resources are limited. Flow control strategies typically employ congestion detection and avoidance algorithms, such as Explicit Congestion Notification (ECN) and window-based flow control. ECN allows routers to notify senders of congestion by setting a specific bit in packet headers, enabling senders to adapt their transmission rates accordingly. Window-based flow control, on the other hand, adjusts the transmission window size based on network conditions to avoid overwhelming the network with excessive traffic. However, the traditional congestion flow control mechanisms designed for the Transmission Control Protocol (TCP) may not be directly employed in some ICN architectures (e.g., CCN or NDN) because the RTT estimation could be inaccurate due to how NDN interest (request packet) and content objects (response packet) work [100; 101]. Furthermore, managing data flow in ICN systems is challenging due to unpredictable user requests, dynamic network conditions, and in-network caching [5; 102]. Dynamic Adaptive Streaming over HTTP (DASH) has become a standard for many streaming services and platforms due to its ability to deliver high-quality media streaming over the Internet despite network congestion or bandwidth fluctuations. DASH dynamically adjusts the quality of the video stream based on the available network bandwidth and the capabilities of the receiving device. However, in ICN, this client-driven bit rate adaptation mechanism may inaccurately gauge available bandwidth, as cached content fragments along transmission paths reduce data retrieval latency. This mismatch between perceived and actual end-to-end bandwidth triggers unstable oscillations in streaming quality, where the system alternates between high and low bit rates [103]. Figure 5 illustrates the bandwidth overestimation problem in DASH over CCN implementation. Suppose the video is available in several different qualities, and the entire file is divided into four segments.