## 5 Enhancing QoS and QoE in ICN
### Transmission and Flow Control
Router \(R_{1}\) has the first segment cached in its local content store, while the entire video is available at the server (producer). When the consumer requests segment #1, router \(R_{1}\) quickly sends the content using its stored copy. Since the request is filled fast from a nearby cache, the user might assume the communication channel has a high bandwidth and then request a higher-quality version of segment #2. But the links between routers \(R_{1}-R_{2}\) and \(R_{2}-R_{3}\) are incapable of supporting the higher bandwidth needed for that better-quality video. This will result in client-side buffer depletion and stalling in video playback and, consequently, a poor QoE. [PERSON] and [PERSON] [104] addressed this throughput estimation problem by proposing a hop-by-hop quality adjustment strategy named HAVS-CCN. The authors introduced a new  
Figure 5: Bandwidth overestimation in CCN. scheduling window (SDQ) for CCN routers, which helps manage data flow by controlling how fast packets are sent at the router level. To adapt video quality, routers organize content in queues based on how they affect video playback--prioritizing critical data to avoid buffering. When cached content or new data need to be sent out, routers use SDQ rules to decide what goes first. Also, instead of relying on standard DASH with AVC encoding, their system uses scalable video coding (SVC) [76], which allows smoother quality adjustments. [PERSON] and [PERSON] [105] introduced a negotiation-based gaming framework to manage the complexities of multiple users simultaneously accessing popular content from distributed network caches while meeting statistically defined QoS criteria. Their multilateral negotiation framework involves bidirectional interactions between players--content repositories and users--who engage in price-based bargaining with all relevant counterparts to maximize their respective utilities. For users, utility is defined as the net benefit of achieved QoS improvements minus incurred expenses, while content stores evaluate utility as revenue from service agreements offset by operational costs. The strategy was evaluated based on the utility outcomes for all participants and the mathematical modeling of consensus likelihood across bargaining scenarios. [PERSON] and [PERSON] [106] studied a multi-source CCN architecture in which data are segmented and dispersed across multiple servers, deviating from traditional single-server storage models. The authors claimed that this distributed caching could help tackle the limited cache size constraint of CCN routers, especially in the case of multimedia streaming. [PERSON] et al. [107] and [PERSON] et al. [108] designed solutions for wireless ICN architectures. In [107], an adaptive retransmission algorithm was proposed for wireless CCN that addresses packet loss and round-trip time (RTT) fluctuations caused by in-network caching. This approach dynamically adjusts retransmission timeouts via sequential hypothesis testing while accounting for packet loss causes (e.g., congestion or signal issues). NS-2 simulations showed the scheme outperformed default CCN and RTO methods [109], achieving better loss recovery and video quality. On the other hand, a real-time content streaming framework for wireless ICN was presented in [108]. This strategy considers the different QoS requirements of media consumers and selects the optimal content provider. Experimental results from the OMNeT++ simulator for an IEEE 802.11g network demonstrated lower delay and jitter and increased throughput. In [110], a DiffServ-based congestion control method was proposed for ICN that adjusts the traffic flow at the router's forwarding interface to improve QoS. The framework classifies ICN traffic into distinct service categories and applies service-category-specific traffic regulation at each network node along the transmission path. Additionally, it uses a resource-availability signaling system to communicate current bandwidth conditions to content requesters.