Streaming Video over Unreliable and Bandwidth Limited Networks

Abstract: The main objective of this thesis is to provide a smooth video playout on the mobile device over wireless networks. The parameters that specify the wireless channel include: bandwidth variation, frame losses, and outage time. These parameters may affect the quality of the video negatively, and the mobile users may notice sudden stops during the playout video, i.e., the picture is momentarily frozen, followed by a jump from one scene to a different one. This thesis focuses on eliminating frozen pictures and reducing the amount of video data that need to be transmitted. In order to eliminate frozen scenes on the mobile screen, we propose three different techniques. In the first technique, the video frames are split into sub-frames; these sub-frames are streamed over different channels. In the second technique the sub-frames will be “crossed” and sent together with other sub-frames that are from different positions in the streaming video sequence. If some sub-frames are lost during the transmission a reconstruction mechanism will be applied on the mobile device to recreate the missing sub-frames. In the third technique, we propose a Time Interleaving Robust Streaming (TIRS) technique to stream the video frames in different order. The benefit of that is to avoid losing a sequence of neighbouring frames. A missing frame from the streaming video will be reconstructed based on the surrounding frames on the mobile device. In order to reduce the amount of video data that are streamed over limited bandwidth channels, we propose two different techniques. These two techniques are based on identifying and extracting a high motion region of the video frames. We call this the Region Of Interest (ROI); the other parts of the video frames are called the non-Region Of Interest (non-ROI). The ROI is transmitted with high quality, whereas the non-ROI is interpolated from a number of references frames. In the first technique the ROI is a fixed size region; we considered four different types of ROI and three different scenarios. The scenarios are based on the position of the reference frames in the streaming frame sequence. In the second technique the ROI is identified based on the motion in the video frames, therefore the size, position, and shape of the ROI will be different from one video to another according to the video characteristic. The videos are coded using ffmpeg to study the effect of the proposed techniques on the encoding size. Subjective and objective metrics are used to measure the quality level of the reconstructed videos that are obtained from the proposed techniques. Mean Opinion Score (MOS) measurements are used as a subjective metric based on human opinions, while for objective metric the Structural Similarity (SSIM) index is used to compare the similarity between the original frames and the reconstructed frames.