CANE: A Cascade Control Approach for Network-Assisted Video QoE Management

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: 1) the network has limited visibility into the client players’ internal state and actions; 2) players’ actions may nullify or negate the network’s actions; and 3) 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 CAscade control-based NEtwork-assisted framework (CANE), a practical network-assisted QoE framework. CANE uses machine learning (ML) techniques to approximate each player’s behavior as a black-box model and model predictive control (MPC) to achieve a near-optimal solution. We evaluate CANE through realistic simulations and show that CANE improves multiplayer QoE fairness by
$\sim $
50% compared with pure client-side adaptive bitrate (ABR) algorithms and by
$\sim $
20% compared with uniform traffic shaping.