## 5 Enhancing QoS and QoE in ICN
### Media-Streaming Strategies  
Traditionally, adaptive streaming has been widely used to improve the consumer QoE for multimedia streaming. Adaptive streaming schemes such as DASH enable video quality adjustment during playback to accommodate the current network conditions, which allows for avoiding bigger issues like stalling. While DASH has been very effective in IP-based networks, new challenges associated with media streaming in ICN make it less effective to implement DASH without modification. For instance, DASH utilizes end-to-end bandwidth estimation, which is difficult in ICN due to content retrieval from multiple sources [115; 116]. In addition, DASH could also suffer from bandwidth overestimation issues illustrated in Figure 5. Furthermore, DASH cannot utilize the content cached at nearby ICN routers' content stores (CSs) as it is designed for a client-server architecture, while ICN focuses on one-to-many content dissemination. Some studies investigating QoS and QoE in ICN from the multimedia-specific \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \# & **Proposal and Publication Year** & **Architecture Environment** & **Experimental Environment** & **Optimized QoS/QoE Metric** \\ \hline
1. & [PERSON] et al. [107], 2013 & CCN & NS-2, WLAN & Loss recovery rate, video quality \\ \hline
2. & [PERSON] and [PERSON] [104], 2016 & CCN & MiniCCNx (based on CCNx 0.8.2) & Playback quality, average delay \\ \hline
3. & [PERSON] and [PERSON] [105], 2016 & ICN & Numerical analysis, wireless network & Statistical QoS \\ \hline
4. & [PERSON] and [PERSON] [106], 2016 & CCN & OPNET, wireless network & Cache hit ratio, bandwidth utilization, server load, content delivery delay, QoE \\ \hline
5. & [PERSON] et al. [108], 2019 & ICN & OMNeT++, MANET & Delay, throughput, jitter \\ \hline
6. & [PERSON] et al. [110], 2022 & ICN & ndnSIM & Throughput, latency, packet loss rate \\ \hline
7. & [PERSON] et al. [112], 2024 & CCN & OMNet++ and cnn-Sim & Throughput, latency \\ \hline
8. & [PERSON] et al. [113], 2024 & ICN & amus-ndnSIM [114] & QoE, delay, bit rate, throughput \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Transmission and flow control-based strategies: architectures, experimental platforms, and QoS/QoE metrics. perspective are described in this section and listed in Table 6. These media-focused studies pay particular attention to QoE due to its importance in multimedia consumption. [PERSON] et al. [115] proposed a CCN adaptive video-streaming framework based on network coding to enable a CCN client to fetch a video from multiple sources using multiple interfaces of a CCN router. The model was tested on the CCNx 0.8.2 platform in conjunction with the CCN-VLC plugin made available by the ITEC-DASH project [117]. Concurrent streaming from multiple sources (two servers with network coding enabled) produced better QoE and higher throughput than streaming from a single server. [PERSON] [118] studied the access time of contents within the content store of a CCN router and how users could exploit it. The author considered a scenario where multiple consumers access the same video or audio content simultaneously. If a terminal deliberately delays access time, it opens up the possibility of exploiting cached data in the CS. [PERSON] et al. [116] investigated the impact of distributed content caching on video streaming in CCN. The authors quantified the number of source switchings during video streaming and the impact on users' QoE. Similar to [118], simultaneous retrieval of the same multimedia content was considered in [116]. A new adaptive video-streaming framework was developed utilizing MOS from human subject tests to guarantee satisfactory QoE. Experimental results derived from the ccnSim simulator and CCNx emulator demonstrated the efficiency of the proposed streaming framework compared to standard DASH and SVC-based streaming in terms of QoE, delay, and bit rate/request. [PERSON] et al. [119] and [PERSON] et al.