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arXiv · 2301.05688

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

Mehdi Hosseinzadeh·Karthick Shankar·Maria Apostolaki·Jay Ramachandran·Steven Adams·Vyas Sekar·Bruno Sinopoli
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Abstract

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.

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Mehdi Hosseinzadeh, Karthick Shankar, Maria Apostolaki, Jay Ramachandran, Steven Adams, Vyas Sekar, Bruno Sinopoli. 2023-01-13. CANE: A Cascade-Control Approach for Network-Assisted Video QoE Management. https://arxiv.org/abs/2301.05688

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