CellFusion: Multipath Vehicle-to-Cloud Video Streaming with Network Coding in the Wild
Yunzhe Ni, Zhilong Zheng, Xianshang Lin, Fengyu Gao, Xuan Zeng, Yirui Liu, Tao Xu, Hua Wang, Zhidong Zhang, Senlang Du, Guang Yang, Yuanchao Su
摘要
This paper presents CellFusion, a system designed for high-quality, real-time video streaming from vehicles to the cloud. It leverages an innovative blend of multipath QUIC transport and network coding. Surpassing the limitations of individual cellular carriers, CellFusion uses a unique last-mile overlay that integrates multiple cellular networks into a single, unified cloud connection. This integration is made possible through the use of in-vehicle Customer Premises Equipment (CPEs) and edge-cloud proxy servers.
In order to effectively handle unstable cellular connections prone to intense burst losses and unexpected latency spikes as a vehicle moves, CellFusion introduces XNC. This innovative network coding-based transport solution enables efficient and resilient multipath transport. XNC aims to accomplish low latency, minimal traffic redundancy, and reduced computational complexity all at once. CellFusion is secure and transparent by nature and does not require modifications for vehicular apps connecting to it.
We tested CellFusion on 100 self-driving vehicles for over six months with our cloud-native back-end running on 50 CDN PoPs. Through extensive road tests, we show that XNC reduced video packet delay by 71.53% at the 99th percentile versus 5G. At 30Mbps, CellFusion achieved 66.11% ∼ 80.62% reduction in video stall ratio versus state-of-the-art multipath transport solutions with less than 10% traffic redundancy.
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引用它的顶会 Paper7
- CellReplay: Towards accurate record-and-replay for cellular networksWilliam Sentosa, Balakrishnan Chandrasekaran, Philip Brighten Godfrey, Haitham HassaniehNSDI 2025 · 被引用 13 次
- STORM: a Multipath QUIC Scheduler for Quick Streaming Media Transport under Unstable Mobile NetworksLiekun Hu, Changlong LiUSENIX ATC 2025 · 被引用 7 次
- Harnessing WebRTC for Large-Scale Live StreamingWei Zhang, Tong Meng, Xianhua Zeng, Wei Yang 等SIGCOMM 2025 · 被引用 4 次
- MAE: More Adaptive Video Encoder for Consistent Low Latency in High-Quality Real-Time CommunicationHua Meng, Yufan Zhuang, Yasna Noushirvani, Xiangjie Huang 等NSDI 2026 · 被引用 3 次
- AnchorNet: Bridging Live and Collaborative Streaming with a Unified ArchitectureTong Meng, Wei Zhang, Dong Chen, Zhen Wang 等USENIX ATC 2025 · 被引用 2 次
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- Server-Driven Video Streaming for Deep Learning InferenceKuntai Du, Ahsan Pervaiz, Xin Yuan, Aakanksha Chowdhery 等SIGCOMM 2020 · 被引用 238 次
- XLINK: QoE-driven multi-path QUIC transport in large-scale video servicesZhilong Zheng, Yunfei Ma, Yanmei Liu, Furong Yang 等SIGCOMM 2021 · 被引用 120 次
- Achieving consistent low latency for wireless real-time communications with the shortest control loopZili Meng, Yaning Guo, Chen Sun, Bo Wang 等SIGCOMM 2022 · 被引用 72 次
- LiveNet: a low-latency video transport network for large-scale live streamingJinyang Li, Zhenyu Li, Ri Lu, Kai Xiao 等SIGCOMM 2022 · 被引用 59 次
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