Region of Interest (ROI) based adaptive cross-layer system for real-time
  video streaming over Vehicular Ad-hoc NETworks (VANETs)
Mohamed Aymen Labiod Mohamed Gharbi François-Xavier Coudoux Patrick Corlay
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.