An elastic DASH-based bitrate adaptation scheme for smooth on-demand video streaming
Togou, Mohammed AmineORCID: 0000-0002-7746-524X and Muntean, Gabriel-MiroORCID: 0000-0002-9332-4770
(2022)
An elastic DASH-based bitrate adaptation scheme for smooth on-demand video streaming.
In: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB'22), 15 - 17 June, 2022, Bilbao, Spain.
The Video traffic has seen a surge in the last decade due to the widespread use of smartphones and the abundance of video streaming applications in the market. Considering the time varying characteristics of today's networks, ensuring high quality of experience (QoE) to all video traffic users has become a daunting challenge for most service providers. The dynamic adaptive streaming over HTTP (DASH) standard enables the adjustment of video bitrates to match the network conditions, therefore guaranteeing smooth video playback. Different DASH-based approaches have been proposed. Nonetheless, most of these schemes incur substantial bitrate oscillations due to their quick reactions to changes in bandwidth, which negatively impact the users' QoE. In this paper, we propose EDRA, a DASH-based bitrate adaption solution that aims at averting video playback interrupts while reducing the number of bitrate switches. EDRA dynamically adjusts the bounds of available video bitrates based on bandwidth estimations. It then selects the most suitable bitrate for each video segment taking into consideration the current and previous bandwidth measurements, the buffer level and the bitrate variation with respect to the previously downloaded segments. Simulation results show that EDRA outperforms existing commercial schemes as it incurs between 6% and 22% higher accumulated played utility and between 30% and 77% lower bitrate switches, ensuring a smooth video streaming experience at high throughput levels.
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Funders:
European Union's Horizon 2020 Research and Innovation programme under Grant Agreement no. 870610 for the TRACTION project, Science Foundation Ireland (SFI) Research Centres Programme Grant Numbers 12/RC/2289\_P2 (Insight SFI Centre for Data Analytics) and 16/SP/3804 (SFI ENABLE Spoke)
ID Code:
27087
Deposited On:
06 May 2022 11:49 by
Mohammed Amine Togou
. Last Modified 06 May 2022 11:49