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Ri Lu

dblp:326/4642 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
0009-0002-6371-6674ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
3 papers
Content delivery and video streaming · 94% Edge and fog computing · 6%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Content delivery and video streaming
content delivery network
1.012026
HCDN: Coordinated Stream Scheduling for Cost-Effective Live Video Delivery · NSDI 2026
Content delivery and video streaming › live streaming
live video delivery
1.012026
HCDN: Coordinated Stream Scheduling for Cost-Effective Live Video Delivery · NSDI 2026
Content delivery and video streaming › content scheduling
streaming scheduling
1.012026
HCDN: Coordinated Stream Scheduling for Cost-Effective Live Video Delivery · NSDI 2026
Content delivery and video streaming
live streaming
0.612022
LiveNet: a low-latency video transport network for large-scale live streaming · SIGCOMM 2022

Methods — techniques the papers use, named apart from their topics

multi-source data plane · 1.0collaborative control plane · 1.0
YearPublicationVenuePosition
2026 RLive: Robust Delivery System for Scaling Live Streaming Services
abstract
As the demand for streaming services surges, content delivery network (CDN) operators face increasing pressure to scale live video delivery without proportionally increasing infrastructure costs. While best-effort edge resources offer a cost-effective extension to traditional CDN capacity, their limited bandwidth and unstable performance pose significant challenges. Our operational experience shows that naively layering such resources onto existing CDN infrastructure falls short in meeting performance and scalability demands. This paper presents RLive, a robust delivery system that scales CDN capacity by integrating best-effort edge resources. RLive features a redundancy-free multi-source data plane to support reliable and cost-efficient live streaming, along with a multi-layer collaborative control plane that combines the global view with local adaptability for scalable user-to-node mapping. Deployed in ByteDance CDN to support large-scale live streaming services with hundreds of millions of daily viewers, RLive has tripled delivery capacity while reducing rebuffering events by 14.9–20.1%.
Yu Tian 0014, Gerui Lv, Qinghua Wu 0004, Ruili Fang, Yajie Peng, Zhichen Xue, Chuanqing Lin, Xiaofei Pang, Ri Lu, Zhenyu Li 0001
EuroSys10
2026 HCDN: Coordinated Stream Scheduling for Cost-Effective Live Video Delivery
Liying Wang 0011, Chengke Wang, Mingming Lu, Qingyue Li, Song Geng, Linsen Wang, Kaida Hu, Haoyuan Huang, Shimao Tian, Ri Lu, Mingfei Hao, Chenren Xu, Shu Shi
NSDI12
2022 LiveNet: a low-latency video transport network for large-scale live streaming
abstract
Low-latency live streaming has imposed stringent latency requirements on video transport networks. In this paper we report on the design and operation of the Alibaba low-latency video transport network, LiveNet. LiveNet builds on a flat CDN overlay with a centralized controller for global optimization. As part of this, we present our design of the global routing computation and path assignment, as well as our fast data transmission architecture with fine-grained control of video frames. The performance results obtained from three years of operation demonstrate the effectiveness of LiveNet in improving CDN performance and QoE metrics. Compared with our prior state-of-the-art hierarchical CDN deployment, LiveNet halves the CDN delay and ensures 98% of views do not experience stalls and that 95% can start playback within 1 second. We further report our experiences of running LiveNet over the last 3 years.
Jinyang Li 0009, Zhenyu Li 0001, Ri Lu, Jufeng Chen, Chunli Zong, Aiyun Chen, Qinghua Wu 0004, Gareth Tyson, Hongqiang Harry Liu
SIGCOMM3