The increasing demand for video streaming services with a high Quality of Experience (QoE) has prompted considerable 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. We evaluate our algorithm against state-of-theart algorithms on large, real-life, crowdsourcing datasets. There are six datasets, each of which contains samples of a single operator (T-Mobile, AT&T or Verizon) from a single road (I100 or I405). All measurements were from Android cellphones. The datasets were provided by WeFi LTD and are public for academic users. Our new algorithm outperforms all other methods in terms of QoE (eMOS). Keywords Dynamic adaptive streaming over HTTP · Adaptic logic · Crowdsourcing · Geo-predictive Communicated by M. Claypool.
On the another hand, geo-predictive crowdsourcing REF has an approach on clients' previous download experience.
Adaptation logic for HTTP dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users
Multimedia Systems
Multimedia Systems
Computer Science