Abstract
Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE). In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering. Throughput prediction plays an important role in helping select the proper video bitrate dynamically. In this paper, we studied the influence of throughput prediction on adaptive video streaming. Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically. However, this is difficult in academic research. Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments. The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming. The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods. However, throughput prediction alone is not enough to ensure high QoE. To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection. By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used. The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.
Original language | English |
---|---|
Article number | 8681430 |
Pages (from-to) | 51346-51356 |
Number of pages | 11 |
Journal | IEEE Access |
Volume | 7 |
DOIs | |
Publication status | Published - 2019 Jan 1 |
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Keywords
- adaptive bitrate control
- DASH
- QoE
- Throughput prediction
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)
- Engineering(all)
Cite this
Evaluation of throughput prediction for adaptive bitrate control using trace-based emulation. / Wei, Bo; Song, Hang; Wang, Shangguang; Kanai, Kenji; Katto, Jiro.
In: IEEE Access, Vol. 7, 8681430, 01.01.2019, p. 51346-51356.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Evaluation of throughput prediction for adaptive bitrate control using trace-based emulation
AU - Wei, Bo
AU - Song, Hang
AU - Wang, Shangguang
AU - Kanai, Kenji
AU - Katto, Jiro
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE). In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering. Throughput prediction plays an important role in helping select the proper video bitrate dynamically. In this paper, we studied the influence of throughput prediction on adaptive video streaming. Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically. However, this is difficult in academic research. Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments. The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming. The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods. However, throughput prediction alone is not enough to ensure high QoE. To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection. By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used. The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.
AB - Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE). In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering. Throughput prediction plays an important role in helping select the proper video bitrate dynamically. In this paper, we studied the influence of throughput prediction on adaptive video streaming. Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically. However, this is difficult in academic research. Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments. The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming. The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods. However, throughput prediction alone is not enough to ensure high QoE. To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection. By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used. The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.
KW - adaptive bitrate control
KW - DASH
KW - QoE
KW - Throughput prediction
UR - http://www.scopus.com/inward/record.url?scp=85066939720&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066939720&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2909399
DO - 10.1109/ACCESS.2019.2909399
M3 - Article
AN - SCOPUS:85066939720
VL - 7
SP - 51346
EP - 51356
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 8681430
ER -