In recent years, streamed 360° videos have gained popularity within Virtual Reality (VR) and Augmented Reality (AR) applications. However, they are of much higher resolutions than 2D videos, causing greater bandwidth consumption when streamed. This increased bandwidth utilization puts tremendous strain on the network capacity of the cloud providers streaming these videos. In this paper, we introduce L3BOU, a novel, three-tier distributed software framework that reduces cloud-edge bandwidth in the backhaul network and lowers average end-to-end latency for 360° video streaming applications. The L3BOU framework achieves low bandwidth and low latency by leveraging edge-based, optimized upscaling techniques. L3BOU accomplishes this by utilizing down-scaled MPEG-DASH-encoded 360° video data, known as Ultra Low Resolution (ULR) data, that the L3BOU edge applies distributed super-resolution (SR) techniques on, providing a high quality video to the client. L3BOU is able to reduce the cloud-edge backhaul bandwidth by up to a factor of 24, and the optimized super-resolution multi-processing of ULR data provides a 10-fold latency decrease in super resolution upscaling at the edge.
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FOCAS: Practical Video Super Resolution using Foveated Rendering
Super-resolution (SR) is a well-studied technique for reconstructing high-resolution (HR) images from low-resolution (LR) ones. SR holds great promise for video streaming since an LR video segment can be transmitted from the video server to the client that then reconstructs the HR version using SR, resulting in a significant reduction in network bandwidth. However, SR is seldom used in practice for real-time video streaming, because the computational overhead of frame reconstruction results in large latency and low frame rate.
To reduce the computational overhead and make SR practical, we propose a deep-learning-based SR method called Fo veated Cas caded Video Super Resolution (focas). focas relies on the fact that human eyes only have high acuity in a tiny central foveal region of the retina. focas uses more neural network blocks in the foveal region to provide higher video quality, while using fewer blocks in the periphery as lower quality is sufficient. To optimize the computational resources and reduce reconstruction latency, focas formulates and solves a convex optimization problem to decide the number of neural network blocks to use in each region of the frame. Using extensive experiments, we show that focas reduces the latency by 50%-70% while maintaining comparable visual quality as traditional (non-foveated) SR. Further, focas provides a 12-16x reduction in the client-to-server network bandwidth in comparison with sending the full HR video segments.
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- PAR ID:
- 10346193
- Date Published:
- Journal Name:
- Proceedings of the 29th ACM International Conference on Multimedia (MM'21)
- Page Range / eLocation ID:
- 5454 to 5462
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3474085.3475673
