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- aggregation classification "A1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2014".
- aggregation format "application/pdf".
- aggregation hasFormat 5733061.bibtex.
- aggregation hasFormat 5733061.csv.
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- aggregation isPartOf urn:issn:1089-7798.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Design and evaluation of a self-learning HTTP adaptive video streaming client".
- aggregation abstract "HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the end-user. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment.".
- aggregation authorList BK1310399.
- aggregation endPage "719".
- aggregation issue "4".
- aggregation startPage "716".
- aggregation volume "18".
- aggregation aggregates 5733075.
- aggregation aggregates 5733076.
- aggregation isDescribedBy 5733061.
- aggregation similarTo LCOMM.2014.020414.132649.
- aggregation similarTo LU-5733061.