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Machine Learning based Bandwidth Prediction for Dynamic Adaptive Streaming over HTTP
Machine Learning based Bandwidth Prediction for Dynamic Adaptive Streaming over HTTP
- 유소영;
- 김경령;
- 김민지;
- 김연진;
- 박소은;
- ... 김동호
초록
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.
키워드
DASH; machine learning; MLP; RNN; ARIMA; HMM; LSTM
- 제목
- Machine Learning based Bandwidth Prediction for Dynamic Adaptive Streaming over HTTP
- 제목 (타언어)
- Machine Learning based Bandwidth Prediction for Dynamic Adaptive Streaming over HTTP
- 저자
- 유소영; 김경령; 김민지; 김연진; 박소은; 김동호
- 발행일
- 2020-12
- 저널명
- 한국정보기술학회 영문논문지
- 권
- 10
- 호
- 2
- 페이지
- 33 ~ 48