ReparoV2: QoE-Aware Live Video Streaming Under Low-Bandwidth Networks

Live video streaming has grown significantly, especially on networks with limited bandwidth. Traditional video streaming methods, which drop less important frames, often struggle with high latency. We propose ReparoV2, a novel approach designed specifically for live streaming environments. On the client side, ReparoV2 selectively omits video frames at the source, which substantially reduces bandwidth usage without significantly degrading the QoE. ReparoV2 implements a real time Video Frame Discarding (VFD) algorithm, which categorizes even-indexed frames into three types: preserving, recovering using neural networks and substituting with the previous frame, which is corresponding to the high, medium and low degree of difference between two adjacent frames. Additionally, to gain a better QoE, ReparoV2 integrates an adaptive bitrate strategy and introduces multiple discrete low-frame-rate encoding modes, balancing video quality and bandwidth reduction when the network bandwidth is limited. On the server side, ReparoV2 uses a Video Frame Interpolation Deep Neural Network (VFI-DNN) and frame-copying to reconstruct dropped frames, ensuring a smooth viewing experience. It updates the VFD model based on received video chunks and sends the updated model back to the client. ReparoV2 outperforms conventional Dynamic Adaptive Streaming over HTTP (DASH), achieving higher Structural Similarity Index Measure (SSIM) (+0.024), lower bandwidth usage (-23.19%), and improved QoE (+26.66%) on 25fps videos under 0.974Mbps average bandwidth.