Bridging the Gap Between Real-time Video and Backlogged Traffic Congestion Control

Abstract

Real-time video applications, such as video conferencing, have become essential to our daily lives, and ensuring reliable and high-quality video delivery in the face of network fluctuation and resource constraints is critical. However, video congestion control algorithms have been criticized for their sub-optimal performance in managing network congestion and maintaining satisfactory video quality and latency. At the same time, state-of-the-art congestion control algorithms have demonstrated remarkable performance improvements, effectively addressing network congestion challenges and enhancing the overall quality of data transmission. In this work, we first demonstrate why there is such a gap between the performance of congestion control schemes on backlogged flows compared to real-time video streams. Second, we present Dumbo, a design for reshaping the video traffic to look like backlogged traffic, thus enabling state-of-the-art delay-sensitive congestion control algorithms for real-time video. We implemented Dumbo atop WebRTC and evaluated it on emulated network conditions using real-world cellular network traces. Our results show that Dumbo in comparison with GCC achieves a 1.5 dB improvement in PSNR, 1.6 dB improvement in SSIM, 100 ms lower frame latency, 35x faster convergence time, 16% increase in the video bitrate, 32% increase in network utilization, and 4x reduction in the network queueing delay.