# Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach
## 4 Performance Evaluation
The percentage of E2 ED encountered is computed considering the maximum allowable delay of 5 s. Thus, the JA-MVS, JMSR, and AMVT schemes obtain values of 2.7%, 2.9%, and 14.0%, respectively. The percentages of performance improvement of the JA-MVS against JMSR and AMVT are 2.0% and 11.3%, respectively. Figure 7b depicts the E2 ED results plotted alongside different data rates in order to assess the performance of the proposed scheme. The proposed JA-MVS has the lowest delay compared to both the JMSR and AMVT schemes. The high delay experienced in AMVT is connected to the high data rate transmitted, which causes queuing of video packets at the transmitting vehicle because of route coupling, which causes a collision. The JMSR and the proposed JA-MVS schemes have an almost equal delay. However, JMSR encounters higher video packet loss. Nevertheless, JA-MVS outperformed the JMSR scheme because it takes into consideration junctions with vehicles moving in opposite directions in the case of the non-availability of a vehicle in the direction of the DVN. The proposed scheme performed better than the AMVT because JA-MVS employs only two paths for the video streaming forwarding, while AMVT utilizes more than two paths, which leads to video packet collision. The average percentage delay encountered is considered based on the maximum allowable delay of 5 s. The JA-MVS, JMSR and AMVT schemes obtain values of 2.9%, 3.2%, and 10.9%, respectively. The percentages of performance improvement of JA-MVS against JMSR and AMVT are 0.3% and 8.0%, respectively. ## 5 Conclusions

In this paper, a junction-aware vehicle selection strategy for multipath video streaming in a vehicular network has been proposed. The JA-MVS scheme transmits video packets considering the different positions of the on-road junctions and the SINR as the signal quality for best forwarding vehicle selection. In the junction-aware algorithm, a vehicle that is ahead of the junction and moving toward the DVN is given higher priority to establish reliable video packet forwarding in the junction area. The SINR is an important metric for evaluating vehicle signal strength considering the urban scenario, which has a lot of obstacles that affect vehicle signal during transmission. The simulation results validate that the JA-MVS scheme significantly improves the video transmission performance in relation to the increase in quality of the video streaming, with a lower PLR and higher SSIM and a

Figure 7: End-to-End Delay based on (**a**) varied numbers of vehicles and (**b**) data rates. decrease in the overall E2 ED of the video packet transmission. In addition, the simulation shows that the overall performance of the JA-MVS outperformed the two baseline schemes of JMSR and AMVT. Moreover, to further extend the proposed scheme, future research should focus on different kinds of roads including highway bridges and bent roads, considering their effects on video data packet forwarding to achieve quality video streaming in VANETs. Conceptualization, A.A. and A.H.A. ; methodology, A.H.A. ; software, I.A. ; validation, H.E.-S., J.L. and M.P. ; formal analysis, A.A.; investigation, I.A. ; resources, A.H.A. ; data curation, H.E.-S.; writing--original draft preparation, A.A.; writing--review and editing, M.P. ; visualization, J.L. ; supervision, A.H.A. ; project administration, I.A. This research was supported by the Roadway, Transportation, and Traffic Safety Research Center (RTISRC) of the United Arab Emirates University (grant number 31R151) and in part by the Science and Technology Program of Guangzhou under Grant 201704030133, A Knowledge-Connection and Cognitive-Style-based Mining System for Massive Open Online Courses and Its Application (PRO16-1300). The authors declare no conflict of interest. ## References

* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Internet of vehicles: Motivation, layered architecture, network model, challenges, and future aspects. _IEEE Access_**2016**, \(4\), 5356-5373. [CrossRef]
* [PERSON] et al.