17 citations · 28 across the 7 of their papers we have counts for
8 papers
SAIL: Perceptual Quality-Aware Rate Control for Cloud Gaming
Houde Qian, Chenglei Wu, Jiaxing Zhang +8
Cloud gaming streams cloud-rendered frames under strict motion-to-photon latency, yet its at-scale viability is increasingly constrained by bandwidth cost: in our study of the T cl…
Decoupling Defense Strategies for Robust Image Watermarking
Jiahui Chen, Zehang Deng, Zeyu Zhang +3
Deep learning-based image watermarking, while robust against conventional distortions, remains vulnerable to advanced adversarial and regeneration attacks. Conventional countermeas…
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…
Towards User-level QoE: Large-scale Practice in Personalized Optimization of Adaptive Video Streaming
Lianchen Jia, Chao Zhou, Chaoyang Li +2
Traditional optimization methods based on system-wide Quality of Service (QoS) metrics have approached their performance limitations in modern large-scale streaming systems. Howeve…
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…
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…
A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction
Ze Meng, Jinnian Zhang, Yumeng Li +3
Modeling powerful interactions is a critical challenge in Click-through rate (CTR) prediction, which is one of the most typical machine learning tasks in personalized advertising a…