The increasing demand for video streaming services with high Quality ofExperience (QoE) has prompted a lot of research on client-side adaptation logicapproaches. However, most algorithms use the client's previous downloadexperience and do not use a crowd knowledge database generated by users of aprofessional service. We propose a new crowd algorithm that maximizes the QoE.Additionally, we show how crowd information can be integrated into existingalgorithms and illustrate this with two state-of-the-art algorithms. Weevaluate our algorithm and state-of-the-art algorithms (including our modifiedalgorithms) on a large, real-life crowdsourcing dataset that contains 336,551samples on network performance. The dataset was provided by WeFi LTD. Our newalgorithm outperforms all other methods in terms of QoS (eMOS).
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