Real-Time Video Streaming Based on TFRC Using Communication Logging for 5G HetNet

Noriyuki SHIMIZU
Hiroaki ASANO
Yoshifumi MORIHIRO
Yukihiko OKUMURA

IEICE TRANSACTIONS on Communications   Vol.E102-B    No.8    pp.1538-1546
Publication Date: 2019/08/01
Publicized: 2019/02/20
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2018TTP0007
Type of Manuscript: Special Section PAPER (Special Section on Technology Trials and Proof-of-Concept Activities for 5G and Beyond)
5G,  HetNet,  video streaming,  TFRC,  

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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.