# A Comparative Case Study of HTTP Adaptive Streaming Algorithms in Mobile Networks

## TL;DR

This paper compares various HTTP Adaptive Streaming algorithms in mobile networks, analyzing their performance and QoE impact based on field data, highlighting the importance of target buffer levels in their effectiveness.

## Contribution

It provides the first comprehensive comparative analysis of main HAS algorithm classes using real-world measurements, offering insights into their performance and design considerations.

## Key findings

- Buffer-based algorithms generally outperform others in QoE.
- Target buffer level critically influences algorithm performance.
- Performance varies notably at low buffer levels in live streaming.

## Abstract

HTTP Adaptive Streaming (HAS) techniques are now the dominant solution for video delivery in mobile networks. Over the past few years, several HAS algorithms have been introduced in order to improve user quality-of-experience (QoE) by bit-rate adaptation. Their difference is mainly the required input information, ranging from network characteristics to application-layer parameters such as the playback buffer. Interestingly, despite the recent outburst in scientific papers on the topic, a comprehensive comparative study of the main algorithm classes is still missing. In this paper we provide such comparison by evaluating the performance of the state-of-the-art HAS algorithms per class, based on data from field measurements. We provide a systematic study of the main QoE factors and the impact of the target buffer level. We conclude that this target buffer level is a critical classifier for the studied HAS algorithms. While buffer-based algorithms show superior QoE in most of the cases, their performance may differ at the low target buffer levels of live streaming services. Overall, we believe that our findings provide valuable insight for the design and choice of HAS algorithms according to networks conditions and service requirements.