Currently, vehicular communications have become a reality used by various applications, especially real time video transmission. However, the video quality received is penalized by the poor characteristics of the transmission channel (availability, non-stationarity, the ration of signal-to-noise, etc.). To improve and ensure minimum video quality at reception end, we propose in this work a mechanism entitled “Secure and Efficient Transmission of Videos in VANET (SETV)”. It's based on the "Quality of Experience (QoE)" and using hierarchical packet management. This later is based on the importance of the images in video streaming. To this end, the use of transmission error correction with uneven error protection has proven to be effective in delivering high quality videos with low network overhead. This is done based on the specific details of video encoding and actual network conditions such as signal to noise ratio, network density, vehicle position and current packet loss rate (PLR) not to mention the prediction of the future DPP. Machine learning models were developed on our work to estimate perceived audio-visual quality. The protocol previously gathers information about its neighboring vehicles to perform distributed jump reinforcement learning. The simulation results obtained for several types of realistic vehicular scenarios show that our proposed mechanism offers significant improvements in terms of video quality on reception and end-to-end delay compared to conventional schemes. The results prove that the proposed mechanism has showed 11% to 18% improvement in video quality and 9% load gain compared to ShieldHEVC.