Content-based Video Adaptation in Low/Variable Bandwidth Communication Networks Using Adaptable Neural Network Structures

In this paper, an adaptable neural network model is used for real time video delivery over communication networks of low and variable bandwidth, such as the wireless ones. The scheme performs video delivery in content domain in contrast to the previous approaches in which only temporal frame skipping is adopted. The proposed method requires no buffering of video frames and thus imposing no frame delay. In particular, in case of low bandwidth conditions, the proposed scheme estimates the number of frames that best represent the sequence within a time segment and transmit this number for delivery instead of a temporal frame skipping. Multiple key frames are considered by optimally approximating the real bandwidth availability with a rational fraction. Key frame estimation is accomplished using a neural network model capable of predicting the indices of the most appropriate key frames that are to be delivered without being available the video information. The model takes into account the previous information as it has been evaluated by the already delivered information. The proposed scheme is based on an efficient recursive estimation algorithm since the network weights cannot be considered constant throughout video transmission. This is due to the fact that content as well as bandwidth characteristics vary from time to time.