  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.
