## 7 Performance Evaluation and Simulation Results  
We have used the open source network simulator ns-2 (v2.27) [29] to implement and evaluate the performance of our proposal named _QoS-aware and social-aware multimetric routing protocol for video-streaming services over MANETs_ (QSMVM). We have carried out a set of simulations with 5 repetitions per each simulation point with independent scenario seeds and all the figures show confidence intervals of 90%. We have used the Bonnmotion tool [30] to generate the MANET scenarios. Furthermore, interfering CBR traffic was generated to constrain the paths. There are two source nodes and two destination nodes, so that each source sends packets related to a video-streaming service. The evaluation is done under two different nodes' densities: (i) a medium nodes' density (100 nodes/km\({}^{2}\)) and (ii) a high nodes' density (200 nodes/km\({}^{2}\)). Simulation settings of the scenarios are shown in Table 3.  
We analyse the effect of \(w_{TS}\) regarding the value of the multimetric score \(MScore_{k}^{i}\) in path \(k\) for iteration \(i\) (see Equation (9)). We make \(w_{TS}\) vary from 0 (i.e., only QoS parameters are considered) to 1 (only the average TS is considered) and see the impact of giving more importance to the level of confidence of a path (\(\overline{TS}\)) over the QoS metrics. Specifically, we analyse the effect in the percentage of packet losses and in the average end-to-end packet delay. We have generated different scenarios regarding the social confidence level between couples of users. Accordingly, we have scenarios from low tie strength values between every couple of users (i.e., a scenario with low social media interaction between users), to scenarios with high tie strength values between couple of users (i.e., a scenario with friends and acquaintances, with a high social media interaction). To obtain the diverse scenarios we generate values of tie strength (TS) between nodes following a normal distribution with a mean taken values from \(\mu=1\) to \(\mu=4\), with standard deviation of \(\sigma=1\) for all the scenarios. The choice of these parameters gives us a very wide range of possible TS values between pairs of individuals in the population considered. As a result, we will obtain a large representative number of different simulation scenarios. Notice that since TS is an asymmetrical measure, TS from a given user \(u\) towards a user \(v\) is different to the TS from user \(v\) towards user \(u\) (\(TS_{u-v}\
eq TS_{v-u}\)). Also, notice that TS between a given mobile node and itself is zero (\(TS_{u-u}=0\)). The simulation settings of the evaluated scenarios are shown in Table 3. The scenarios used to test the proposal consists of a set of 27 and 54 mobile nodes distributed in a MANET of 520 m \(\times\) 520 m, producing nodes' densities of 100 and for 200 nodes/km\({}^{2}\), respectively. The transmission range of the nodes is 120 m. Nodes move with a speed up to 2 m/s. Two video flows are transmitted from node \(S_{1}\) to \(D_{1}\) and from node \(S_{2}\) to \(D_{2}\), respectively. Figures 4-19 show the average percentage of packet losses and the average end-to-end packet delay, for a TS normal distribution whose parameters are \(\sigma=1\) and \(\mu=1\) to \(\mu=4\). Notice that the percentage of packet losses, which show values around 20%, apparently might seem quite high. However, we must contextualize those results to the inherent low-connectivity feature of MANETs. MANET nodes constantly move, which produces frequent link breakages and consequently packet losses increase. To cope with this, our multimetric proposal MMDSR notably outperforms vanilla distance-based DSR, showing an improvement around 10 dB in the PSNR [7]. We compare the results to the case of not considering the average tie strength \(\overline{TS}\) (\(w_{TS}=0\)) in the path selection process, i.e., we always select the path with best QoS performance without taking the level of confidence among forwarding nodes into account.