113 citations · 365 across the 14 of their papers we have counts for
15 papers
HiVid: LLM-Guided Video Saliency For Content-Aware VOD And Live Streaming
Jiahui Chen, Bo Peng, Lianchen Jia +3
Content-aware streaming requires dynamic, chunk-level importance weights to optimize subjective quality of experience (QoE). However, direct human annotation is prohibitively expen…
Crucible: Quantifying the Potential of Control Algorithms through LLM Agents
Lianchen Jia, Chaoyang Li, Qian Houde +3
Control algorithms in production environments typically require domain experts to tune their parameters and logic for specific scenarios. However, existing research predominantly f…
Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models
Lianchen Jia, Chaoyang Li, Ziqi Yuan +4
Over the past decade, adaptive video streaming technology has witnessed significant advancements, particularly driven by the rapid evolution of deep learning techniques. However, t…
Reducing Traffic Wastage in Video Streaming via Bandwidth-Efficient Bitrate Adaptation
Hairong Su, Shibo Wang, Shusen Yang +2
Bitrate adaptation (also known as ABR) is a crucial technique to improve the quality of experience (QoE) for video streaming applications. However, existing ABR algorithms suffer f…
MSADM: Large Language Model (LLM) Assisted End-to-End Network Health Management Based on Multi-Scale Semanticization
Fengxiao Tang, Xiaonan Wang, Xun Yuan +4
Network device and system health management is the foundation of modern network operations and maintenance. Traditional health management methods, relying on expert identification…
Optimizing Adaptive Video Streaming with Human Feedback
Tianchi Huang, Rui-Xiao Zhang, Chenglei Wu +1
Quality of Experience~(QoE)-driven adaptive bitrate (ABR) algorithms are typically optimized using QoE models that are based on the mean opinion score~(MOS), while such principles…
Self-play Reinforcement Learning for Video Transmission
Tianchi Huang, Rui-Xiao Zhang, Lifeng Sun
Video transmission services adopt adaptive algorithms to ensure users' demands. Existing techniques are often optimized and evaluated by a function that linearly combines several w…
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning
Xin Yao, Tianchi Huang, Chenglei Wu +2
Human beings are able to master a variety of knowledge and skills with ongoing learning. By contrast, dramatic performance degradation is observed when new tasks are added to an ex…
Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating
Xin Yao, Tianchi Huang, Rui-Xiao Zhang +2
Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices. Federated averaging (FedAvg), the…
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs
Xin Yao, Tianchi Huang, Chenglei Wu +2
Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things…
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning
Tianchi Huang, Chao Zhou, Rui-Xiao Zhang +3
Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding strategies without any presumptions, has become one of the research hotspots for adaptive streaming. Howe…
Reactive Video Caching via long-short-term fusion approach
Rui-Xiao Zhang, Tianchi Huang, Chenglei Wu +1
Video caching has been a basic network functionality in today's network architectures. Although the abundance of caching replacement algorithms has been proposed recently, these me…
Tiyuntsong: A Self-Play Reinforcement Learning Approach for ABR Video Streaming
Tianchi Huang, Xin Yao, Chenglei Wu +3
Existing reinforcement learning~(RL)-based adaptive bitrate~(ABR) approaches outperform the previous fixed control rules based methods by improving the Quality of Experience~(QoE)…
Delay-Constrained Rate Control for Real-Time Video Streaming with Bounded Neural Network
Tianchi Huang, Rui-Xiao Zhang, Chao Zhou +1
Rate control is widely adopted during video streaming to provide both high video qualities and low latency under various network conditions. However, despite that many work have be…
QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning
Tianchi Huang, Rui-Xiao Zhang, Chao Zhou +1
Due to the fluctuation of throughput under various network conditions, how to choose a proper bitrate adaptively for real-time video streaming has become an upcoming and interestin…