When they go high, we go low: low-latency live streaming in dash.js with LoL

Title When they go high, we go low: low-latency live streaming in dash.js with LoL
Author Lim, M., Akçay, Mehmet Necmettin, Bentaleb, A., Beğen, Ali Cengiz, Zimmermann, R.
Publication Date: 2020-05
Publication Place - The ACM Digital Library
Subject HAS, ABR, DASH, CMAF, Low-latency, HTTP chunked transfer encoding, Adaptive playout, SOM, Learning
Type Document
Language English
Digital Yes
Manuscript No
Library: Özyeğin University
Library Asset ID 978-145036845-2
Record ID 6f8c75f2-3e64-411c-b59c-4212b513de1b
Library Location Computer Science
Date 2020-05
Notes Ministry of Education - Singapore
Sample Text Live streaming remains a challenge in the adaptive streaming space due to the stringent requirements for not just quality and rebuffering, but also latency. Many solutions have been proposed to tackle streaming in general, but only few have looked into better catering to the more challenging low-latency live streaming scenarios. In this paper, we re-visit and extend several important components (collectively called Low-on-Latency, LoL) in adaptive streaming systems to enhance the low-latency performance. LoL includes bitrate adaptation (both heuristic and learning-based), playback control and throughput measurement modules.
DOI 10.1145/3339825.3397043
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When they go high, we go low: low-latency live streaming in dash.js with LoL

Author Lim, M., Akçay, Mehmet Necmettin, Bentaleb, A., Beğen, Ali Cengiz, Zimmermann, R.
Publication Date 2020-05
Publication Place - The ACM Digital Library
Subject HAS, ABR, DASH, CMAF, Low-latency, HTTP chunked transfer encoding, Adaptive playout, SOM, Learning
Type Document
Language English
Digital Yes
Manuscript No
Library Özyeğin University
Library Asset ID 978-145036845-2
Record ID 6f8c75f2-3e64-411c-b59c-4212b513de1b
Library Location Computer Science
Date 2020-05
Notes Ministry of Education - Singapore
Sample Text Live streaming remains a challenge in the adaptive streaming space due to the stringent requirements for not just quality and rebuffering, but also latency. Many solutions have been proposed to tackle streaming in general, but only few have looked into better catering to the more challenging low-latency live streaming scenarios. In this paper, we re-visit and extend several important components (collectively called Low-on-Latency, LoL) in adaptive streaming systems to enhance the low-latency performance. LoL includes bitrate adaptation (both heuristic and learning-based), playback control and throughput measurement modules.
DOI 10.1145/3339825.3397043
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