Region of Interest (ROI) based adaptive cross-layer system for real-time video streaming over Vehicular Ad-hoc NETworks (VANETs)
This addresses video quality issues for real-time vehicle applications like obstacle detection in VANETs, representing an incremental improvement over existing methods.
The paper tackles the problem of poor video transmission quality in vehicular networks by proposing an adaptive cross-layer system that prioritizes regions of interest (ROI) in video streams, achieving PSNR gains up to 11dB on ROI parts for HEVC compressed video.
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.