Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming
arXiv:2505.01584v4 Announce Type: replace Abstract: Adaptive video streaming optimizes Quality of Experience (QoE) metrics by selecting appropriate bitrates according to varying network bandwidth and user demands. In practice, however, real-world network bandwidth often exhibits heterogeneity relative to training environments. Current methods predominantly tackle this problem through learning-based approaches designed to improve generalization performance. While our systematic investigation reveals a critical limitation: neural networks suffer from plasticity loss, significantly impeding their
The paper identifies a critical limitation (plasticity loss) in neural networks for adaptive video streaming, a problem that is increasingly relevant as demand for high-quality streaming grows and real-world network conditions vary.
This research highlights a fundamental challenge in applying deep reinforcement learning to dynamic real-world problems, suggesting that current learning-based approaches may hit inherent limitations without addressing plasticity.
The focus for improving adaptive video streaming via AI shifts from simply better generalization techniques to fundamental neural network architecture and training methods that preserve plasticity.
- · AI researchers focusing on neural network plasticity
- · Video streaming platforms that adopt advanced DRL techniques
- · Consumers with improved video Quality of Experience
- · DRL algorithms not accounting for plasticity loss
- · Developers solely relying on generalization metrics for DRL performance
Adaptive video streaming algorithms will need to integrate plasticity-preserving mechanisms to perform robustly in diverse network environments.
This could lead to new architectural paradigms in deep reinforcement learning beyond video streaming, applicable to other domains requiring continuous adaptation.
Enhanced streaming quality and reliability could further accelerate the shift to online content consumption, potentially impacting traditional broadcast media.
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Read at arXiv cs.LG