Nowadays, real-time vehicle applications increasingly rely on video acquisition and processing to detect or even identify vehicles and obstacles in the driving environment. In this letter, we propose an algorithm that allows reinforcing these operations by improving end-to-end video transmission quality in a vehicular context. The proposed low complexity solution gives highest priority to the scene regions of interest (ROI) on which the perception of the driving environment is based on. This is done by applying an adaptive cross-layer mapping of the ROI visual data packets at the IEEE 802.11p MAC layer. Realistic VANET simulation results demonstrate that for HEVC compressed video communications, the proposed system offers PSNR gains up to 11dB on the ROI part.

翻译：如今，实时车辆应用日益依赖视频采集与处理，以检测甚至识别驾驶环境中的车辆和障碍物。本文提出一种算法，通过提升车载场景下的端到端视频传输质量来强化上述功能。该低复杂度方案赋予场景中驾驶环境感知所依赖的感兴趣区域（ROI）最高优先级，具体通过在IEEE 802.11p MAC层实施ROI视觉数据包的自适应跨层映射实现。基于真实VANET仿真结果表明，对于HEVC压缩视频通信，所提系统在ROI部分的PSNR增益最高可达11dB。