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Haoyi Fang

dblp:423/5796 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0004-1111-0747ORCID · corroborated

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

Computer networks · 2 · 2 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
1 paper
Transport protocols and congestion control · 70% Content delivery and video streaming · 23% Network measurement and analytics · 7%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Transport protocols and congestion control › error control
automatic repeat request
1.012026
AutoRec: Accelerating Loss Recovery for Live Streaming in a Multi-Supplier Market · IEEE Trans. Netw. 2026
Content delivery and video streaming
live streaming
1.012026
AutoRec: Accelerating Loss Recovery for Live Streaming in a Multi-Supplier Market · IEEE Trans. Netw. 2026
Transport protocols and congestion control
loss recovery
1.012026
AutoRec: Accelerating Loss Recovery for Live Streaming in a Multi-Supplier Market · IEEE Trans. Netw. 2026
Transport protocols and congestion control
retransmission delay
1.012026
AutoRec: Accelerating Loss Recovery for Live Streaming in a Multi-Supplier Market · IEEE Trans. Netw. 2026

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

measurement study · 1.0QUIC implementation · 1.0
YearPublicationVenuePosition
2026 Multi-CDN as a Collective Service: Towards Hot Start in Congestion Control at Scale
Tong Li 0014, Jiuxiang Zhu, Bo Wu 0002, Haoyi Fang, Ke Xu 0002
APNet5
2026 AutoRec: Accelerating Loss Recovery for Live Streaming in a Multi-Supplier Market
abstract
Due to the limited permissions for upgrading dual-side (i.e., server-side and client-side) loss tolerance schemes from the perspective of CDN vendors in a multi-supplier market, modern large-scale live streaming services are still using the automatic-repeat-request (ARQ) based paradigm for loss recovery, which only requires server-side modifications. In this paper, we first conduct a large-scale measurement study with up to 50 million live streams. We find that loss showsdynamicsand live streaming contains frequenton-off mode switchingin the wild. We further find that the recovery latency, enlarged by the ubiquitous retransmission loss, is a critical factor affecting live streaming’s client-side QoE (e.g., video freezing). We then propose an enhanced recovery mechanism called AutoRec, which can transform the disadvantages of on-off mode switching into an advantage for reducing loss recovery latency without any modifications on the client side. AutoRec allows users to customize overhead tolerance and recovery latency tolerance and adaptively adjusts strategies as the network environment changes to ensure that recovery latency meets user demands whenever possible while keeping overhead under control. We implement AutoRec upon QUIC and evaluate it via testbed and real-world commercial services deployments. The experimental results demonstrate the practicability and profitability of AutoRec.
Tong Li 0014, Bo Wu 0002, Fuyu Wang 0006, Jiuxiang Zhu, Haoyi Fang, Xinle Du, Ke Xu 0002
IEEE Trans. Netw.7