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

TL;DR

CANE is a practical cascade-control framework that leverages machine learning and model predictive control to enhance multiplayer video QoE fairness in network-assisted environments, overcoming visibility and conflicting objectives.

Contribution

The paper introduces CANE, a novel cascade-control-based network-assisted QoE management framework that effectively models client behavior and optimizes fairness.

Findings

CANE improves multiplayer QoE fairness by ~50% over client-side algorithms.

CANE outperforms uniform traffic shaping by ~20%.

The approach demonstrates significant gains in realistic simulations.

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.