The increasing demand for video streaming services with high Quality of Experience (QoE) has prompted a lot of research on client-side adaptation logic approaches. However, most algorithms use the client's previous download experience and do not use a crowd knowledge database generated by users of a professional service. We propose a new crowd algorithm that maximizes the QoE. Additionally, we show how crowd information can be integrated into existing algorithms and illustrate this with two state-of-the-art algorithms. We evaluate our algorithm and state-of-the-art algorithms (including our modified algorithms) on a large, real-life crowdsourcing dataset that contains 336,551 samples on network performance. The dataset was provided by WeFi LTD. Our new algorithm outperforms all other methods in terms of QoS (eMOS).
@article{arxiv.1602.02030,
title = {Adaptation Logic for HTTP Dynamic Adaptive Streaming using Geo-Predictive Crowdsourcing},
author = {Ran Dubin and Amit Dvir and Ofir Pele and Ofer Hadar and Itay Katz and Ori Mashiach},
journal= {arXiv preprint arXiv:1602.02030},
year = {2017}
}