TwinStar: A Practical Multi-path Transmission Framework for Ultra-Low Latency Video Delivery
Haiping Wang, YU Zhen-hua, Rui-Xiao Zhang, Siping Tao, Hebin Yu, Shu Shi · 2023
Ultra-low latency video streaming has received explosive growth in the past few years. However, existing methods all focus on single-path transmission, which is ineffective in dealing with really poor network conditions. To tackle their problems, we propose TwinStar, a novel multi-path framework to improve the experience quality of ultra-low latency video. The core idea of TwinStar is to concurrently leverage multiple paths to mitigate the negative impacts of network jitter on a single path. In particular, by carefully designing the video encoding, data allocation and loss recovery, TwinStar is very robust to handle network dynamics and deliver high-quality video services. We have deployed TwinStar in a commercial cloud gaming platform and evaluated it with real-world networks. The extensive experiments demonstrate that TwinStar significantly outperforms the single-path transmission methods, with 91% reduction in stall ratio and 11% improvement in PSNR across all regions.