# Imitation Learning for Adaptive Video Streaming with Future Adversarial Information Bottleneck Principle

arXiv: https://arxiv.org/abs/2405.03692 · HTML: https://mlsanity.com/paper/2405.03692/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 9 of 9)

- [IQ-Learn: Inverse soft-Q Learning for Imitation](https://mlsanity.com/paper/2106.12142/) (2021)
- [Parrot: Data-Driven Behavioral Priors for Reinforcement Learning](https://mlsanity.com/paper/2011.10024/) (2020)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards](https://mlsanity.com/paper/2008.07773/) (2020)
- [Task-Oriented Communication for Multi-Device Cooperative Edge Inference](https://mlsanity.com/paper/2109.00172/) (2021)
- [BOLA: Near-Optimal Bitrate Adaptation for Online Videos](https://mlsanity.com/paper/1601.06748/) (2016)
- [CBA: Contextual Quality Adaptation for Adaptive Bitrate Video Streaming (Extended Version)](https://mlsanity.com/paper/1901.05712/) (2019)
- [ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration](https://mlsanity.com/paper/2308.10068/) (2023)
- [Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR Streaming](https://mlsanity.com/paper/2210.12191/) (2022)

## Abstract (arXiv)

Adaptive video streaming plays a crucial role in ensuring high-quality video streaming services. Despite extensive research efforts devoted to Adaptive BitRate (ABR) techniques, the current reinforcement learning (RL)-based ABR algorithms may benefit the average Quality of Experience (QoE) but suffers from fluctuating performance in individual video sessions. In this paper, we present a novel approach that combines imitation learning with the information bottleneck technique, to learn from the complex offline optimal scenario rather than inefficient exploration. In particular, we leverage the deterministic offline bitrate optimization problem with the future throughput realization as the expert and formulate it as a mixed-integer non-linear programming (MINLP) problem. To enable large-scale training for improved performance, we propose an alternative optimization algorithm that efficiently solves the MINLP problem. To address the issues of overfitting due to the future information leakage in MINLP, we incorporate an adversarial information bottleneck framework. By compressing the video streaming state into a latent space, we retain only action-relevant information. Additionally, we introduce a future adversarial term to mitigate the influence of future information leakage, where Model Prediction Control (MPC) policy without any future information is employed as the adverse expert. Experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing the quality of adaptive video streaming, providing a 7.30% average QoE improvement and a 30.01% average ranking reduction.