# Learning from Experience: A Dynamic Closed-Loop QoE Optimization for Video Adaptation and Delivery

## TL;DR

This paper introduces a dynamic closed-loop framework that uses user feedback to learn and optimize QoE for video delivery, accounting for individual user perceptions and improving overall experience.

## Contribution

It presents a novel closed-loop control system that adapts video quality based on subjective user feedback, addressing heterogeneity in user perceptions.

## Key findings

- System converges to a steady state with improved QoE
- User feedback-driven optimization enhances personalized video quality
- Framework effectively learns and adapts to individual user preferences

## Abstract

The quality of experience (QoE) is known to be subjective and context-dependent. Identifying and calculating the factors that affect QoE is indeed a difficult task. Recently, a lot of effort has been devoted to estimate the users QoE in order to improve video delivery. In the literature, most of the QoE-driven optimization schemes that realize trade-offs among different quality metrics have been addressed under the assumption of homogenous populations. Nevertheless, people perceptions on a given video quality may not be the same, which makes the QoE optimization harder. This paper aims at taking a step further in order to address this limitation and meet users profiles. To do so, we propose a closed-loop control framework based on the users(subjective) feedbacks to learn the QoE function and optimize it at the same time. Our simulation results show that our system converges to a steady state, where the resulting QoE function noticeably improves the users feedbacks.