Prior efforts have shown that network-assisted schemes can improve the Quality-of-Experience (QoE) and QoE fairness when multiple video players compete for bandwidth. However, realizing network-assisted schemes in practice is challenging, as: i) the network has limited visibility into the client players' internal state and actions; ii) players' actions may nullify or negate the network's actions; and iii) the players' objectives might be conflicting. To address these challenges, we formulate network-assisted QoE optimization through a cascade control abstraction. This informs the design of CANE, a practical network-assisted QoE framework. CANE uses machine learning techniques to approximate each player's behavior as a black-box model and model predictive control to achieve a near-optimal solution. We evaluate CANE through realistic simulations and show that CANE improves multiplayer QoE fairness by ~50% compared to pure client-side adaptive bitrate algorithms and by ~20% compared to uniform traffic shaping.
翻译:先前的努力表明,网络辅助计划可以在多个视频播放器竞争带宽时提高经验质量(QoE)和QoE公平性。然而,在实践中实现网络辅助计划具有挑战性,因为:(一) 网络在客户参与者的内部状态和行动中的可见度有限;(二) 参与者的行动可能抵消或否定网络的行动;(三) 参与者的目标可能相互矛盾。为了应对这些挑战,我们通过级联控制抽象来制定网络辅助QoE优化。这为CANE的设计提供了参考,这是一个实用的网络辅助QoE框架。CANE使用机器学习技术来将每个玩家的行为作为黑盒模型和模型预测控制,以达到接近最佳的解决方案。我们通过现实的模拟对CANE进行评估,并表明CANE提高了多玩家QoE的公平性,比纯客户方的适应性位率算法高出了50%,比统一交通结构高出了约20%。