Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve the user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly deployed in client devices with limited resources, such as mobile phones. Existing solutions suffer from a trade-off between algorithm performance and deployment overhead. To make the deployment of sophisticated ABR algorithms practical, we propose PiTree , a general , high-performance , and scalable framework that can faithfully convert sophisticated ABR algorithms into decision trees with teacher-student learning. In this way, network operators can train complex models offline and deploy converted lightweight decision trees online. We also present theoretical analysis on the conversion and provide two upper bounds of the prediction error during the conversion and the generalization loss after conversion. Evaluation on three representative ABR algorithms with both trace-driven emulation and real-world experiments demonstrates that PiTree could convert ABR algorithms into decision trees with < 3% average performance degradation. Moreover, compared to original deployment solutions, PiTree could save considerable operating expenses for content providers.

中文翻译：

---

通过师生学习实际部署重量级自适应比特率算法

主要的商业客户端视频播放器采用自适应比特率（ABR）算法来提高用户体验质量（QoE）。随着ABR算法的发展，越来越复杂的方法（例如神经网络）已被采用以追求更好的性能。但是，这些复杂的方法过于繁重，无法直接部署在资源有限的客户端设备（例如移动电话）中。现有解决方案在算法性能和部署开销之间进行权衡。为了使复杂的ABR算法的部署切实可行，我们建议皮树 ， 一种 一般的 ， 高性能的 ， 和 可扩展的该框架可通过师生学习将忠实的ABR算法忠实地转换为决策树。这样，网络运营商可以离线训练复杂的模型，并在线部署转换后的轻量级决策树。我们还提供了有关转换的理论分析，并提供了转换期间的预测误差和转换后的泛化损失的两个上限。通过跟踪驱动的仿真和实际实验对三种代表性的ABR算法进行评估，结果表明：皮树可以将ABR算法转换为决策树，且平均性能下降<3％。而且，与原始部署解决方案相比，皮树 可以为内容提供商节省可观的运营费用。