## 6 Using MEC as Enabler for Vehicular Applications
### Video Streaming  
V2X communication creates links between cars and infrastructure, people, or other cars. Digital media services have grown significantly over the last several years, and this rise is anticipated to continue as more devices use infotainment services in the future. As a result, it is crucial to concentrate on client metrics, such as video quality [170]. Under a specific circumstance involving substantial data traffic, the combination of MEC and VNs can improve the entire network's capacity and dependability for video communication [171]. Some recent studies are investigated in the rest of this section. To discover and select the ideal car from which to acquire data streams among those in the neighborhood, scientists propose a 5G MEC-assisted car discovery service that centralizes all the navigation data in a local region [172]. It keeps track of recent location-specific data in order to recommend a set of sensor data sources for video streaming that considers the prospective location of the vehicles. Instead of broadcast messaging, the suggested strategy uses a MEC service to collect the information needed to provide vehicle discovery requests with low latency and high throughput. The vehicle identification system uses a simple algorithm to choose the best cars to provide sources of sensor data by considering network delay and car mobility. Even with other types of network delay like the LTE networks, the MEC-assisted vehicle identification service described in this study has effective results. As the received information is limited to a region near the end user, the MEC not only centralizes vehicle requests but also offers advantages in terms of security and confidentiality of data. Researchers, in [173], suggest an approach to make use of both caching and computation capabilities at the MEC nodes to fulfill users' queries for videos with various bandwidths. Most current works on video streaming are not adaptive bitrate aware and are primarily concerned with storing and transferring data without performing any computation. To fulfill user queries, MEC hosts can convert a video into many versions. Each version is a distinct bitrate copy of the stream, and lower bitrate versions can be created from higher bitrate ones [174]. They broaden the concept of cooperative streaming to a new level where the MEC hosts can perform the delivery role for the requested video over backhaul connections [173]. Also, MEC hosts can convert the video into the preferred version. For instance, when the target server is overloaded, other servers can assist to carry out the encoding process. To save bandwidth and reduce data communication times, researchers, in [175], suggest an architecture for video streaming in VNs where car users are able to obtain high-resolution video from the MEC servers installed on RSUs. For users in vehicles, they create a model that considers the average download percentage and weighs it for assessing the mean opinion score of mobile videos. Due to the fact that the car-to-RSU association lasts for a short time, it is possible for a car user to only obtain a small portion of the streaming while under the coverage of an RSU, which results in poor QoE. The incomplete portion of the video copies will still be distributed by a Macro Base Station (MBS) after the vehicle user departs the RSU service range region. The authors model the video-buffering problem as an MDP, and propose a smart buffering algorithm based on DRL. In ref. [176], authors investigate a buffering method with the objective of optimizing the data size received from MEC hosts located in RSUs rather than MBSs, taking into account the high mobility of cars and the overlapped coverage of RSUs. The main goal is to store the information as close as possible to cars. The architecture considers one MBS and several RSUs, each one regularly updating the MBS with information about the state of its service coverage. The MBS acts as a centralized repository.