  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).
