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

arXiv: https://arxiv.org/abs/2301.05688 · HTML: https://mlsanity.com/paper/2301.05688/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2023.

## Built on by (most cited first, 1 of 1)

- [Provably-Stable Neural Network-Based Control of Nonlinear Systems](https://mlsanity.com/paper/2502.00248/) (2025)

## Abstract (arXiv)

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.