Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4955856 | Journal of Network and Computer Applications | 2017 | 15 Pages |
Abstract
In this paper, we detail the design of the proposed ML-based framework and compare its performance with other benchmarking HAS solutions, under various video streaming scenarios. Particularly, we show through extensive experimentation that the proposed approach can reduce video freezes and freeze time with about 65% and 45% respectively, when compared to benchmarking algorithms. These results represent a major improvement for the QoE of the users watching multimedia content online.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Stefano Petrangeli, Tingyao Wu, Tim Wauters, Rafael Huysegems, Tom Bostoen, Filip De Turck,