The fifth-generation (5G) mobile communication system is being researched and developed for launch as a commercial service in 2020. The 5G mobile network will include many radio access technologies, such as LTE, 5G NR, and WLAN. Therefore, a user equipment (UE) will be connected to different types of base stations as it moves within a 5G heterogeneous network. Accordingly, it is assumed that the throughput will change with each change in the serving cell. The 5G mobile network is expected to serve large capacity contents, such as 4K videos. However, a conventional video streaming method cannot effectively use the available bandwidth in a 5G heterogeneous network. In this study, we propose a sending rate adaptation method based on predictions for the available bandwidth. In the proposed method, the available bandwidth is predicted from the communication log data. The communication logging database, including past throughput with its location, is created by a UE. A UE refers to the communication log data for predictions when the serving cell is likely to change. We develop a video streaming device that implements the proposed method and evaluates its performance. The results show that the proposed method can change the sending rate and resolution according to the available bandwidth. The proposed method increases the probability of transmitting high-resolution video, which is not possible with conventional methods. Moreover, we performed subjective evaluation of the transmitted video by the proof-of-concept test. The result of the subjective evaluation shows that the proposed method improves the quality of experience for video streaming.
Takumi HIGUCHI
Panasonic Corporation
Hideki SHINGU
Panasonic Corporation
Noriyuki SHIMIZU
Panasonic Corporation
Takeshi MIYAGOSHI
Panasonic Corporation
Hiroaki ASANO
Panasonic Corporation
Yoshifumi MORIHIRO
NTT DOCOMO, INC.
Yukihiko OKUMURA
NTT DOCOMO, INC.
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Takumi HIGUCHI, Hideki SHINGU, Noriyuki SHIMIZU, Takeshi MIYAGOSHI, Hiroaki ASANO, Yoshifumi MORIHIRO, Yukihiko OKUMURA, "Real-Time Video Streaming Based on TFRC Using Communication Logging for 5G HetNet" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 8, pp. 1538-1546, August 2019, doi: 10.1587/transcom.2018TTP0007.
Abstract: The fifth-generation (5G) mobile communication system is being researched and developed for launch as a commercial service in 2020. The 5G mobile network will include many radio access technologies, such as LTE, 5G NR, and WLAN. Therefore, a user equipment (UE) will be connected to different types of base stations as it moves within a 5G heterogeneous network. Accordingly, it is assumed that the throughput will change with each change in the serving cell. The 5G mobile network is expected to serve large capacity contents, such as 4K videos. However, a conventional video streaming method cannot effectively use the available bandwidth in a 5G heterogeneous network. In this study, we propose a sending rate adaptation method based on predictions for the available bandwidth. In the proposed method, the available bandwidth is predicted from the communication log data. The communication logging database, including past throughput with its location, is created by a UE. A UE refers to the communication log data for predictions when the serving cell is likely to change. We develop a video streaming device that implements the proposed method and evaluates its performance. The results show that the proposed method can change the sending rate and resolution according to the available bandwidth. The proposed method increases the probability of transmitting high-resolution video, which is not possible with conventional methods. Moreover, we performed subjective evaluation of the transmitted video by the proof-of-concept test. The result of the subjective evaluation shows that the proposed method improves the quality of experience for video streaming.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018TTP0007/_p
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@ARTICLE{e102-b_8_1538,
author={Takumi HIGUCHI, Hideki SHINGU, Noriyuki SHIMIZU, Takeshi MIYAGOSHI, Hiroaki ASANO, Yoshifumi MORIHIRO, Yukihiko OKUMURA, },
journal={IEICE TRANSACTIONS on Communications},
title={Real-Time Video Streaming Based on TFRC Using Communication Logging for 5G HetNet},
year={2019},
volume={E102-B},
number={8},
pages={1538-1546},
abstract={The fifth-generation (5G) mobile communication system is being researched and developed for launch as a commercial service in 2020. The 5G mobile network will include many radio access technologies, such as LTE, 5G NR, and WLAN. Therefore, a user equipment (UE) will be connected to different types of base stations as it moves within a 5G heterogeneous network. Accordingly, it is assumed that the throughput will change with each change in the serving cell. The 5G mobile network is expected to serve large capacity contents, such as 4K videos. However, a conventional video streaming method cannot effectively use the available bandwidth in a 5G heterogeneous network. In this study, we propose a sending rate adaptation method based on predictions for the available bandwidth. In the proposed method, the available bandwidth is predicted from the communication log data. The communication logging database, including past throughput with its location, is created by a UE. A UE refers to the communication log data for predictions when the serving cell is likely to change. We develop a video streaming device that implements the proposed method and evaluates its performance. The results show that the proposed method can change the sending rate and resolution according to the available bandwidth. The proposed method increases the probability of transmitting high-resolution video, which is not possible with conventional methods. Moreover, we performed subjective evaluation of the transmitted video by the proof-of-concept test. The result of the subjective evaluation shows that the proposed method improves the quality of experience for video streaming.},
keywords={},
doi={10.1587/transcom.2018TTP0007},
ISSN={1745-1345},
month={August},}
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TY - JOUR
TI - Real-Time Video Streaming Based on TFRC Using Communication Logging for 5G HetNet
T2 - IEICE TRANSACTIONS on Communications
SP - 1538
EP - 1546
AU - Takumi HIGUCHI
AU - Hideki SHINGU
AU - Noriyuki SHIMIZU
AU - Takeshi MIYAGOSHI
AU - Hiroaki ASANO
AU - Yoshifumi MORIHIRO
AU - Yukihiko OKUMURA
PY - 2019
DO - 10.1587/transcom.2018TTP0007
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2019
AB - The fifth-generation (5G) mobile communication system is being researched and developed for launch as a commercial service in 2020. The 5G mobile network will include many radio access technologies, such as LTE, 5G NR, and WLAN. Therefore, a user equipment (UE) will be connected to different types of base stations as it moves within a 5G heterogeneous network. Accordingly, it is assumed that the throughput will change with each change in the serving cell. The 5G mobile network is expected to serve large capacity contents, such as 4K videos. However, a conventional video streaming method cannot effectively use the available bandwidth in a 5G heterogeneous network. In this study, we propose a sending rate adaptation method based on predictions for the available bandwidth. In the proposed method, the available bandwidth is predicted from the communication log data. The communication logging database, including past throughput with its location, is created by a UE. A UE refers to the communication log data for predictions when the serving cell is likely to change. We develop a video streaming device that implements the proposed method and evaluates its performance. The results show that the proposed method can change the sending rate and resolution according to the available bandwidth. The proposed method increases the probability of transmitting high-resolution video, which is not possible with conventional methods. Moreover, we performed subjective evaluation of the transmitted video by the proof-of-concept test. The result of the subjective evaluation shows that the proposed method improves the quality of experience for video streaming.
ER -