# QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach

## TL;DR

This paper proposes a machine learning-based framework for predicting viewer distribution in crowdsourced live streaming to optimize resource allocation, improve QoE, and reduce costs through proactive, geo-distributed cloud infrastructure management.

## Contribution

It introduces a prediction-driven resource allocation approach that leverages viewer location data to enhance QoE and cost-efficiency in live streaming services.

## Key findings

- Predicted viewer numbers closely match actual data.
- Optimized resource allocation reduces access delay.
- Trade-off analysis between delay and cost.

## Abstract

Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers quality of experience (QoE) is the key to maximize the audiences number and increase streaming providers' profits. This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls. Moreover, allocating the exact needed resources beforehand avoids over-provisioning, which may lead to significant costs by the service providers. In the contrary, under-provisioning might cause significant delays to the viewers. In this paper, we introduce a prediction driven resource allocation framework, to maximize the QoE of viewers and minimize the resource allocation cost. First, by exploiting the viewers locations available in our unique dataset, we implement a machine learning model to predict the viewers number near each geo-distributed cloud site. Second, based on the predicted results that showed to be close to the actual values, we formulate an optimization problem to proactively allocate resources at the viewers proximity. Additionally, we will present a trade-off between the video access delay and the cost of resource allocation.