5 papers
ReForge: Keeping ABR Algorithms Never Finished with Verified Large Language Model Edits
Zhiqiang He, Zhi Liu
Designing an ABR algorithm for one network scenario takes an engineer months, and large language models now do this work in hours, matching or beating hand-built designs. But eithe…
NSMA: Neuro-Symbolic Manifold Alignment for Generalizable Adaptive Bitrate Streaming under Texture Shift
Zhiqiang He, Zhi Liu
For decades, ABR has kept two kinds of intelligence apart. Neural policies learn rich behaviors yet forget them the moment the environment changes; rules never learn, and never for…
EMS-FL: Federated Tuning of Mixture-of-Experts in Satellite-Terrestrial Networks via Expert-Driven Model Splitting
Angzi Xu, Zezhong Zhang, Zhi Liu +1
The rapid advancement of large AI models imposes stringent demands on data volume and computational resources. Federated learning, though designed to exploit distributed data and c…
Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming
Zhiqiang He, Zhi Liu
Adaptive video streaming optimizes Quality of Experience (QoE) metrics by selecting appropriate bitrates according to varying network bandwidth and user demands. In practice, howev…
Plasticity-Aware Mixture of Experts for Learning Under QoE Shifts in Adaptive Video Streaming
Zhiqiang He, Zhi Liu
Adaptive video streaming systems are designed to optimize Quality of Experience (QoE) and, in turn, enhance user satisfaction. However, differences in user profiles and video conte…