Author
Bentaleb, A., Beğen, Ali Cengiz, Harous, S., Zimmermann, R.
Publication Date
2021
Publication Place
-
IEEE
Subject
ABR, Bandwidth prediction, Chunked transfer encoding, CMAF, DASH, HTTP adaptive streaming, Low latency
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1520-9210
Record ID
9aaecf4c-1bc3-4d85-88c3-12a3e8592da4
Library Location
Computer Science
Date
2021
Notes
Ministry of Education, Singapore ; UAE University
Sample Text
Today's HTTP adaptive streaming solutions use a variety of algorithms to measure the available network bandwidth and predict its future values. Bandwidth prediction, which is already a difficult task, must be more accurate when lower latency is desired due to the shorter time available to react to bandwidth changes, and when mobile networks are involved due to their inherently more frequent and potentially larger bandwidth fluctuations. Any inaccuracy in bandwidth prediction results in flawed adaptation decisions, which will in turn translate into a diminished viewer experience. We propose an Automated Model for Prediction (AMP) that encompasses techniques for bandwidth prediction and model auto-selection specifically designed for low-latency live steaming with chunked transfer encoding. We first study statistical and computational intelligence techniques to implement a suite of bandwidth prediction models that can work accurately under a broad range of network conditions, and second, we introduce an automated prediction model selection method. We confirm the effectiveness of our solution through trace-driven live streaming experiments.
DOI
10.1109/TMM.2020.3013387
Cilt
23
