Adaptive video streaming uploading with moving prediction in VANETs scenarios

Video streaming uploading service over Vehicular Networks is very useful as it can support many applications. Due to the vehicle´s high mobility and roadside access point (AP)´s sparse deployment, how to provide a high-quality video service with a low-price charge still remains an open question. To address this issue, a novel video uploading scheme based on vehicle moving prediction is proposed, in which the vehicle-to-infrastructure (V2I) and vehicle-to-vehicle communications (V2V) are cooperated to forward video streaming continuously from the moving vehicles to a fixed network. We make use of vehicle mobility prediction to find the stable relay nodes and reduce the link failure frequency, while an adaptive selection algorithm is used for roadside APs and gateway-vehicles to reduce the charge for video uploading. Experimental results show that the proposed scheme can achieve good performance while keeping a low application charge.