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Machine Learning based Bandwidth Prediction for Dynamic Adaptive Streaming over HTTPMachine Learning based Bandwidth Prediction for Dynamic Adaptive Streaming over HTTP
- Other Titles
- Machine Learning based Bandwidth Prediction for Dynamic Adaptive Streaming over HTTP
- Issue Date
- Dec-2020
- Publisher
- 한국정보기술학회
- Keywords
- DASH; machine learning; MLP; RNN; ARIMA; HMM; LSTM
- Citation
- 한국정보기술학회 영문논문지, v.10, no.2, pp 33 - 48
- Pages
- 16
- Indexed
- KCICANDI
- Journal Title
- 한국정보기술학회 영문논문지
- Volume
- 10
- Number
- 2
- Start Page
- 33
- End Page
- 48
- ISSN
- 2234-1072
2234-0963
- Abstract
- By Digital Transformation, new technologies like ML (Machine Learning), Big Data, Cloud, VR/AR are being used to video streaming technology. We choose ML to provide optimal QoE (Quality of Experience) in various network conditions. In other words, ML helps DASH in providing non-stopping video streaming. In DASH, the source video is segmented into short duration chunks of 2–10 seconds, each of which is encoded at several different bitrate levels and resolutions. We built and compared the performances of five prototypes after applying five different machine learning algorithms to DASH. The prototype consists of a dash.js, a video processing server, web servers, data sets, and five machine learning models.
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Related Researcher
- Kim, Dong Ho
- Software Education Institute