Adaptation Logic for HTTP Dynamic Adaptive Streaming using Geo-Predictive Crowdsourcing
This addresses video streaming quality issues for users, but it is incremental as it builds on existing adaptation logic.
The paper tackles the problem of improving video streaming Quality of Experience by proposing a new crowd algorithm that uses geo-predictive crowdsourcing, and it outperforms state-of-the-art methods on a real-life dataset with 336,551 samples.
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).