Research on Vehicle-to-Road Collaboration and End-to-End Collaboration for Multimedia Services in the Internet of Vehicles

Abstract

With the development of mature communication network technologies such as 5G, edge computing and slicing, business applications in areas such as Telematics and intelligent transportation also need the support of vehicle-road collaboration technologies. Due to the mobility of vehicles and the heterogeneity of communication interfaces, it brings many challenges for data transmission and processing of Telematics. Therefore, network resources, roadside unit devices and sunken edge nodes need to work together to guarantee real-time network communication. Thus, they can jointly help improve the quality of network services and user experience, and show the level of vehicle-road collaboration wisdom in terms of safety, efficiency and service. In view of the future 5G network-related technologies and C-V2X development trend, this paper provides a comprehensive technical solution framework for Telematics based on end-edge collaboration, combining network resource deployment optimization and edge caching technologies for video transmission tasks in Telematics application scenarios. By adding 5G base station type RSU and UAV mobile base station to work jointly, network resources are better allocated for scalable coded video, and a relatively better edge caching strategy is obtained based on user mobility analysis. Aiming to improve the network system performance, the vehicle-side, road-side and edge-side are jointly considered to improve the utilization efficiency of limited network resources. In this paper, we hope to provide a practical and effective service architecture for Telematics video application scenarios through a convergence scheme of network resource allocation and edge caching configuration.

A Survey on Video Streaming for Next-Generation Vehicular Networks

“…Zhou et al [199] explored multi-user caching in vehicular MEC networks, where edge servers with caching and computing capabilities support task computing from vehicle users. Ma and Sun [208] proposed an architecture incorporating end-edge collaboration, edge caching technology, and network resource deployment optimization for video transmission in vehicles. Their architecture involves 5G base stations and drone mobile base station configurations to transmit SVC videos with analyzed user mobility, obtaining improved edge caching strategies.…”

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

“…In End-to-End slicing networks to maximise the heterogeneous needs, the same technique is presented in [26] to govern multi-agent slices. Additionally, Markov chain algorithms proposed in [27] on Vehicle-to-Road slice to deal with video application scenarios to improve multimedia services during video transmission to improve QoS, Quality of Experience (QoE), the efficiency and safety for the 5G slices while the vehicle is moving from one location to another. In addition, feedback from 5G users was gathered via an online survey platform to identify potential tactics for 5G-VINNI stakeholders' experimentationas-a-service [28].…”

Challenges and Opportunities for Multimedia Transmission in Vehicular Ad Hoc Networks: A Comprehensive Review

“…Despite the importance of multimedia transmission in VANETs, there are practical considerations and implementation challenges to address [41]. There is a need for VANET system design and architecture to accommodate the requirements of multimedia transmission [42], ensuring efficient data exchange, synchronization, and integration with existing communication technologies.…”