## 3 Road-Based Multi-Metric Forwarder Evaluation Scheme
### Multi-Metric Video Streaming with Dynamic Self-Weightting Score
#### 3.2.2 Multi-Metric Video Streaming with Dynamic Self-Weighting Score  
In this subsection, the multi-metric score is further enhanced to be dynamically self-configured for obtaining a more realistic score in order to weight the multiple metrics. In most of the existing multi-metric forwarder schemes, equal or varied fixed weight value has always been assigned without considering the change in the network condition. Considering the nature of VANETs and most especially the urban scenario where there are several obstructions, there is a need to adapt the multi-metric forwarder scheme to be dynamic in operation. The multi-metrics are made to be self-configured, that is, dynamic. The dynamic weighting score mechanism assigns weight to the different metrics based on the network situation. The dynamic weight score is better than the fixed variable or equal value weighting because a metric is usually important based on a certain situation in the VANETs environment. In selecting an optimal NFV, the PFV need to evaluate the optimality of the NFV. Moreover, several metrics are considered. In most of the previous work, the metrics are usually given equal or varied fixed value based on some assumption of perceived importance. However, this is not always true because some metrics become more important based on the current network situation. For example, considering VDR and SINR, the score for SINR might  
Figure 3: Packet Forwarding-based Road-based Multi-metric Forwarder Evaluation scheme for Multipath Video Streaming (RMF-MVS) Flowchart. need to be given higher value compared to that of the vehicle density of the road when the vehicle is at the junction area, because of many obstructing objects. Both the SINR and vehicle trajectory are important metrics but in different situations. Therefore, a Road-based Multi-metric Forwarder evaluation for Multipath Video Streaming with Dynamic Self-Weighting (RMF-MVS + DSW) has been developed, which adapts to the network situation. The dynamic weight distribution categorizes vehicles in an enhanced pattern by assigning each metric a certain priority depending on the most recent network situations. In the DSW, higher weight score is assigned to the metric that has a lower value, which tends to zero, while metrics with a higher value closer to one are given lower weight. The whole idea is that the network conditions are collected and weights are provided in such a way that it adapts to the variable of the network condition. With this concept, the important metrics based on the network condition will not be assigned a smaller weight. This concept is carried out on all C-NFVs. Thus, it enhances the selection of optimal NFV. The overall weights of all metrics are not greater than one (1). The PFV categorizes the C-NFV from the most suitable to the less suitable by employing the multi-metric score mechanism. The weights of the metrics can be assigned as \(\text{Weights}\ =\ \text{wt}_{1}\), \(\text{wt}_{2}\), \(\ldots\), \(\text{wt}_{n}\), the n represent the number of metrics, since the number of metrics utilized are five (5), \(\text{n}=5\). These different metrics are calculated by the algorithm in order to have a dynamic update considering the current network situation of the C-NFV. The concept is to point out the metrics that can lead to better selection of the NFV among the C-NFVs. The five metrics are represented as \(\text{Metrics}\ =\ \text{mt}_{1}\), \(\text{mt}_{2}\), \(\text{mt}_{3}\), \(\text{mt}_{4}\), \(\text{mt}_{5}\) which are equal to VDR, VTJ, \(\text{H}_{\text{m}}\text{R}_{\text{r}}\), \(\text{ABE}\ \text{and}\ \text{SINR}\), respectively. Hence, the variation value of each metrics is represented as \(\text{VR}_{\text{m}}\) between the time interval of \(\text{t}_{0}\) and \(\text{t}_{1}\), such that \(\text{t}_{1}>\text{t}_{0}\), derived as follows in Equation (17). \[\text{VR}_{\text{m}}=\left\{