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 content lead to different weights for QoE factors, resulting in user-specific QoE functions and, thus, varying optimization objectives. This variability poses significant challenges for neural networks, as they often struggle to generalize under evolving targets - a phenomenon known as plasticity loss that prevents conventional models from adapting effectively to changing optimization objectives. To address this limitation, we propose the Plasticity-Aware Mixture of Experts (PA-MoE), a novel learning framework that dynamically modulates network plasticity by balancing memory retention with selective forgetting. In particular, PA-MoE leverages noise injection to promote the selective forgetting of outdated knowledge, thereby endowing neural networks with enhanced adaptive capabilities. In addition, we present a rigorous theoretical analysis of PA-MoE by deriving a regret bound that quantifies its learning performance. Experimental evaluations demonstrate that PA-MoE achieves a 45.5% improvement in QoE over competitive baselines in dynamic streaming environments. Further analysis reveals that the model effectively mitigates plasticity loss by optimizing neuron utilization. Finally, a parameter sensitivity study is performed by injecting varying levels of noise, and the results align closely with our theoretical predictions.

翻译：自适应视频流系统旨在优化体验质量，进而提升用户满意度。然而，用户画像与视频内容的差异导致体验质量因子的权重分配不同，从而产生用户特定的体验质量函数及随之变化的优化目标。这种多变性对神经网络构成了显著挑战，因为网络在动态变化的目标下往往难以保持泛化能力——这种现象被称为可塑性损失，它阻碍传统模型有效适应不断变化的优化目标。为克服这一局限，我们提出可塑性感知的专家混合模型，这是一种通过平衡记忆保持与选择性遗忘来动态调节网络可塑性的新型学习框架。具体而言，该模型利用噪声注入促进对过时知识的选择性遗忘，从而赋予神经网络更强的自适应能力。此外，我们通过推导量化其学习性能的遗憾界，对可塑性感知的专家混合模型进行了严格的理论分析。实验评估表明，在动态流媒体环境中，该模型相比基准方法实现了45.5%的体验质量提升。进一步分析显示，该模型通过优化神经元利用率有效缓解了可塑性损失。最后，通过注入不同强度的噪声进行参数敏感性研究，实验结果与理论预测高度吻合。