Optimizing 360 video delivery over cellular networks

By Feng Qian, Lusheng Ji, Bo Han, Vijay Gopalakrishnan. ATC@MobiCom, 2016

432 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f15a66c8d035d65a3cda124a4abf9d65d5110823

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Viewport-adaptive navigable 360-degree video delivery (2016, background): “[17], who showed that prediction accuracy drops for time periods greater than 2 s.”
Flare: Practical Viewport-Adaptive 360-Degree Video Streaming for Mobile Devices (2018, methodology): “Several prior studies indicate that VR users’ head movement is indeed predictable [23, 48].”
Toward Low-Latency and Ultra-Reliable Virtual Reality (2018, background): “Recent studies have shown that VR gaming users’ head movement can be predicted with high accuracy for upcoming hundreds of milliseconds [3].”
Wireless Edge Computing With Latency and Reliability Guarantees (2019, methodology): “We leverage results from previous works as in [107] that state that the user’s future pose in the next hundreds of milliseconds can be predicted with high accuracy to proactively predict, render, and cache the users upcoming frames, subject to computation and storage resource availability.”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, methodology): “ulting deformation changes in a virtual environment. In [179], a deep neural network based algorithm is proposed to automatically convert 2D videos and images to a stereoscopic 3D format. The work in [180] proposed a linear regression learning algorithm to predict the users’ head movement in VR appl”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “The work in [180] proposed a linear regression learning algorithm to predict the users’ head movement in VR applications.”
Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale (2018, methodology): “In this regard, we propose a proactive computing and multi-connectivity (MC) solution, which is motivated by recent findings on users’ predictions with high accuracy for an upcoming prediction window of hundreds of milliseconds [35].”