## 8 Conclusions and Future Work  
Providing real-time multimedia services over MANETs represent a very promising and attractive topic due to its interesting applications and challenges. In this work, we have presented a new proposal named _QoS-aware and social-aware multimetric routing protocol for video-streaming services over MANETs_ (QSMVM). We have shown that we can take advantage of the growing omnipresence of the social web technologies through the information of interaction among users in social networks to design routing protocols for MANETs. Our goal is to achieve a good trade-off between QoS and confidence in the forwarding path established by our self-configured dynamic framework to offer video-streaming services over MANETs. Simulation results show the benefits of our approach in terms of QoS provided (percentage of packet losses and average end-to-end packet delay) and average tie strength measured in the forwarding path. In this work, we have modeled a social tie strength (TS) that follows a normal distribution with a given average \(\mu\) and a standard deviation \(\sigma\). Benefices are specially noticeable under a high nodes' density (200 nodes/km\({}^{2}\)) with a high social media interaction between enough pairs of users. We have analysed the effect of the weight of the social confidence provided by the forwarding path (\(w_{TS}\) in Equation (9)) in front of the weight of the QoS provided by the forwarding path (\(w_{QoS}\) in Equation (9)), looking to the suitable \(w_{TS}\) value that balances a good trade-off between QoS and social confidence. The goal is to give a high importance to the level of trust among users forming the forwarding paths through which packets are routed, although till a maximum \(w_{TS}\) that guarantees that we do not have a big impact in the QoS. In this article, we have emulated diverse scenarios from (i) scenarios with users without social interaction between users (low tie strength values, e.g., a big city like New York), to (ii) scenarios with high social interaction between users (high tie strength values, e.g., a University Campus). We have modeled the tie strength (TS) values with a normal distribution with means \(\mu=1\), 2, 3 and 4 and standard deviation of \(\sigma=1\). A good suggestion for future work would be to carry out simulations in a scenario with real values of the tie strength gathered from social networks such as Twitter or Facebook. In this work, we have given the same weight \(w_{QoS}\) to all the QoS parameters (see Equation (9)). Thus, in this work we have analysed the trade-off between a global measure of the QoS and the \(\overline{TS}\). That is, we have given the same importance to all QoS parameters (available bandwidth, packet losses, packet delay, delay jitter, hop-count distance to destination, reliability metric, mobility metric). We are currently working on the design of different weights to those QoS metrics, e.g., giving higher values to the percentage of packet losses or packet delay so that they have a higher impact on the selection of forwarding paths, which may be interesting, depending on the applications. Figure 19: Average end-to-end packet delay. Scenario with very high social media interaction (TS normal distribution with \(\mu=4\) and \(\sigma=1\)). Nodes’ density = 200 nodes/km\({}^{2}\). **Author Contributions:** Conceptualization, R.P.D.R. and M.A.I. ; methodology, A.M.M. and M.A.I. ; software, A.M.M and E.P.J. ; validation, A.M.M. and M.A.I. ; investigation, R.P.D.R. and A.F.-V.; writing--original draft preparation, A.M.M and E.P.J. ; writing--review and editing, R.P.D.R. and M.A.I. ; supervision, A.M.M. and A.F.-V.; funding acquisition, R.P.D.R. All authors have read and agreed to the published version of the manuscript.