Optimizing 360° Video Streaming to Head-Mounted Virtual Reality

Streaming 360 ° videos to Head-Mounted Displays (HMDs) is very challenging due to large video sizes, stringent real-time requirements, and complex human visual systems. In my PhD study, we focus on three core problems to optimize 360 ° video streaming to HMDs. First, we adopt sensor and content data...

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Bibliographic Details
Published in2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) pp. 458 - 459
Main Authors Fan, Ching-Ling, Hsu, Cheng-Hsin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2018
Subjects
Bit rate
Feature extraction
Prediction algorithms
Predictive models
Quality of experience
Solid modeling
Streaming media
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Summary:Streaming 360 ° videos to Head-Mounted Displays (HMDs) is very challenging due to large video sizes, stringent real-time requirements, and complex human visual systems. In my PhD study, we focus on three core problems to optimize 360 ° video streaming to HMDs. First, we adopt sensor and content data to design a fixation prediction algorithm. Second, we develop a user study to construct a 360° Quality-of-Experience (QoE) model. Last, we design, implement, and evaluate an optimal joint tile selection and bitrate allocation algorithm that leverages both the fixation prediction algorithm and QoE model. Initial results are reported in this paper.
DOI:10.1109/PERCOMW.2018.8480355