Abstract
Live streaming services inside the Internet-of-Vehicles (IoV) are becoming more significant in vehicle entertainment systems as a result of the fast-paced growth of the automotive industry and communications technologies. At the same time, with the rapid development of in-vehicle video, the development of broadcasting business has been driven. The 3GPP standardization group has suggested the evolved Multimedia Broadcast Multicast Service (eMBMS), which by multicasting video services to vehicle users, can significantly enhance the utilization of spectrum resources and improve signal quality. Real-time video is sent by eMBMS over synchronized spectral resources of nearby roadside units (RSUs) using a single frequency networks (SFN). However, the video transmission rate depends on the vehicle users with the lowest channel state information, to overcome these limitations, the vehicle users can be divided into groups in the SFN. It is incredibly difficult to maximize the user quality of service (QoS) of a vehicle due to dynamic nature of IoV’s radio resources and channels. To overcome this obstacle, we employ a model SFN video transmission strategy that, while grouping vehicles, jointly optimizes group-oriented bit rate selection and resource allocation to decrease latency and bit rate switch in IoV. The Markov decision process (MDP), which takes into account the time-varying features of wireless channels, is used to describe the joint optimization problem. Then after, the abovementioned MDP is handled by using soft actor-critic (SAC) algorithm. The proposed approach may substantially enhance video quality while reducing latency and bit rate switch, according to extensive simulation findings based on actual data. It also works well in terms of learning efficiency and stability.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 62261019 and in part by the Fundamental Research Program of Shanxi Province under Grants 202103021224024 and 202103021223021.
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Wang, L., Fu, F. (2023). Optimizing Video QoS for eMBMS Users in the Internet of Vehicles. In: Yu, C., Zhou, J., Song, X., Lu, Z. (eds) Green, Pervasive, and Cloud Computing. GPC 2022. Lecture Notes in Computer Science, vol 13744. Springer, Cham. https://doi.org/10.1007/978-3-031-26118-3_17
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DOI: https://doi.org/10.1007/978-3-031-26118-3_17
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