Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
| Autor: | Joongheon Kim, Kyeong Seon Kim, Aziz Mohaisen, Dohyun Kwon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Context (language use) 02 engineering and technology Video quality Machine learning computer.software_genre lcsh:Technology Multiclass classification lcsh:Chemistry 0202 electrical engineering electronic engineering information engineering General Materials Science Quality of experience Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes Live video business.industry lcsh:T Process Chemistry and Technology General Engineering 020206 networking & telecommunications Provisioning softmax lcsh:QC1-999 Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Softmax function 020201 artificial intelligence & image processing Multinomial distribution Artificial intelligence QoE business lcsh:Engineering (General). Civil engineering (General) computer lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 9, Iss 11, p 2297 (2019) Applied Sciences Volume 9 Issue 11 |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|