Abstract:

In order to enhance the video transmission quality in cellular community， to strengthen the visual perception ability of the blind area in intelligent connected vehicles， and hence to improve driving safety and handiness， a reliable strategy for achieving real?time video stream transmission under the environment of long?term evolution?vehicle to everything （LTE?V2X） is proposed in this paper. First of all， for improving the system performance of cellular community and the image quality of real?time video stream transmission， a resource allocation algorithm is put forward for maximizing vehicle to vehicle communication throughput， while meeting the requirements of heterogeneous service quality for V2V user under the state of imperfect information channel. The algorithm adopts the first?order autoregressive model for the modeling of real information channel gain， and the power distribution and the multiple access and pairing of spectra are carried out step?by?step to complete the resource allocation. Then， scalable video coding technique is used to encode real?time video streams， the encoded video data frames having different video quality level are transmitted via network， and based on the perceived data from network， video stream buffer is set up. Finally， adaptive transmission algorithm is designed to calculate the current optimal quality level of transmitted video， and the quality of video images transmitted can be dynamically adjusted according to the communication network environment， so the chance of video playback freezing is effectively reduced. The results of experiment show that compared with video transmission algorithm RALSB having fundamental layer protection and video quality adaptive selection （VQAS） algorithm， the strategy proposed can effectively enhance the quality of real?time video stream and reduce the chance of video playback freezing.

Key words: LTE?V2X, resource allocation, scalable video coding, real?time video stream