ZTE Communications ›› 2022, Vol. 20 ›› Issue (4): 96-109.DOI: 10.12142/ZTECOM.202204012

• Research Paper • Previous Articles     Next Articles

A Content-Aware Bitrate Selection Method Using Multi-Step Prediction for 360-Degree Video Streaming

GAO Nianzhen1, YU Yifang2, HUA Xinhai2, FENG Fangzheng1, JIANG Tao1()   

  1. 1.Huazhong University of Science and Technology, Wuhan 430074, China
    2.ZTE Corporation, Shenzhen 518057, China
  • Received:2021-12-12 Online:2022-12-31 Published:2022-12-30
  • About author:GAO Nianzhen received her bachelors’ degree in computer science and technology from Sichuan University, China in 2020. She is currently working toward the PhD degree with the Research Center of 6G Mobile Communications, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, China. Her research interests include multimedia transmission and mobile edge computing.|YU Yifang received his MS degree in engineering from Xi’an Jiaotong University, China. He currently serves as the Senior Vice President of ZTE Corporation and President of the Cloud Video and Energy Product Operation Division. He has engaged in market planning and operations management in the telecommunications industry for over 20 years. His research interests include traditional telecom networks as well as the emerging fields such as cloud computing and the mobile Internet.|HUA Xinhai received his PhD degree from Nanjing University, China. He is currently the Vice President of ZTE Corporation and General Manager of Cloud Video Product Department. His research interests include cloud computing, IP-based video product technology and solutions, video business security solutions, content distribution network technology, product solutions, etc.|FENG Fangzheng received his bachelors’ degree in communication engineering from Hunan University, China in 2019. He is currently working toward the PhD degree with the Research Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Hubei, China. His research interests include wireless communication and mobile multimedia transmission.|JIANG Tao (taojiang@hust.edu.cn) received his PhD degree in information and communication engineering from Huazhong University of Science and Technology, China in 2004. He is currently a Distinguished Professor with the Research Center of 6G Mobile Communications and School of Cyber Science and Engineering, Huazhong University of Science and Technology. He has authored or coauthored more than 300 technical papers in main journals and conferences, and nine books or chapters in the areas of communications and networks. He has served or is serving as an associate editor of some technical journals in communications, including the IEEE Network, IEEE Transactions on Signal Processing, IEEE Communications Surveys and Tutorials, IEEE Transactions on Vehicular Technology, and IEEE Internet of Things Journal, and he is an associate editor-in-chief of China Communications. His main research directions include wireless communication, mobile multimedia transmission, etc.
  • Supported by:
    ZTE Corporation(2021420118000065)


A content-aware multi-step prediction control (CAMPC) algorithm is proposed to determine the bitrate of 360-degree videos, aiming to enhance the quality of experience (QoE) of users and reduce the cost of video content providers (VCP). The CAMPC algorithm first employs a neural network to generate the content richness and combines it with the current field of view (FOV) to accurately predict the probability distribution of tiles being viewed. Then, for the tiles in the predicted viewport which directly affect QoE, the CAMPC algorithm utilizes a multi-step prediction for future system states, and accordingly selects the bitrates of multiple subsequent steps, instead of an instantaneous state. Meanwhile, it controls the buffer occupancy to eliminate the impact of prediction errors. We implement CAMPC on players by building a 360-degree video streaming platform and evaluating other advanced adaptive bitrate (ABR) rules through the real network. Experimental results show that CAMPC can save 83.5% of bandwidth resources compared with the scheme that completely transmits the tiles outside the viewport with the Dynamic Adaptive Streaming over HTTP (DASH) protocol. Besides, the proposed method can improve the system utility by 62.7% and 27.6% compared with the DASH official and viewport-based rules, respectively.

Key words: DASH, content-aware FOV prediction, bitrate adaptation, multi-step prediction, generalized predictive control