Personalized online live video streaming using softmax-based multinomial classification
- Authors
- Kim, K.; Kwon, D.; Kim, J.; Mohaisen, A.
- Issue Date
- Jun-2019
- Publisher
- MDPI AG
- Keywords
- QoE; Softmax
- Citation
- Applied Sciences (Switzerland), v.9, no.11
- Journal Title
- Applied Sciences (Switzerland)
- Volume
- 9
- Number
- 11
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/33193
- DOI
- 10.3390/app9112297
- ISSN
- 2076-3417
2076-3417
- Abstract
- As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms. © 2019 by the authors.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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