Resource Allocation for Improved User Experience with Live Video Streaming in 5G

TL;DR

This paper presents a theoretical framework for resource allocation in 5G networks to enhance live video streaming quality, achieving at least 15% performance improvement over existing methods.

Contribution

It introduces a novel analytical approach for optimizing resource allocation in 5G to improve user experience in live video streaming, addressing multiple quality objectives.

Findings

Performance improved by at least 15%

Framework effectively balances resolution and user count

Validated through simulations on real traces

Abstract

Providing a high-quality real-time video streaming experience to mobile users is one of the biggest challenges in cellular networks. This is due to the need of these services for high rates with low variability, which is not easy to accomplish given the competition among (usually a high number of) users for constrained network resources and the high variability of their channel characteristics. A way of improving the user experience is by exploiting their buffers and the ability to provide a constant data rate to everyone, as one of the features of 5G networks. However, the latter is not very efficient. To this end, in this paper we provide a theoretical-analysis framework for resource allocation in 5G networks that leads to an improved user experience when watching live video. We do this by solving three problems, in which the objectives are to provide the highest achievable video resolution to all one-class and two-class users, and to maximize the number of users that experience a given resolution. The analysis is validated by simulations that are run on traces. We also compare the performance of our approach against other techniques for different QoE metrics. Results show that the performance can be improved by at least 15% with our approach.