The emerging volumetric videos offer a fully immersive, six degrees of freedom (6DoF) viewing experience, at the cost of extremely high bandwidth demand. In this paper, we design, implement, and evaluate Vues, an edge-assisted transcoding system that delivers high-quality volumetric videos with low bandwidth requirement, low decoding overhead, and high quality of experience (QoE) on mobile devices. Through an IRB-approved user study, we build a f irst-of-its-kind QoE model to quantify the impact of various factors introduced by transcoding volumetric content into 2D videos. Motivated by the key observations from this user study, Vues employs a novel multiview approach with the overarching goal of boosting QoE. The Vues edge server adaptively transcodes a volumetric video frame into multiple 2D views with the help of a few lightweight machine learning models and strategically balances the extra bandwidth consumption of additional views and the improved QoE, indicated by our QoE model. The client selects the view that optimizes the QoE among the delivered candidates for display. Comprehensive evaluations using a prototype implementation indicate that Vues dramatically outperforms existing approaches. On average, it improves the QoE by 35% (up to 85%), compared to single-view transcoding schemes, and reduces the bandwidth consumption by 95%, compared to the state-of-the-art that directly streams volumetric videos.
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CUP: Cellular Ultra-light Probe-based Available Bandwidth Estimation
WiFi has emerged as a pivotal technology for delivering Quality of Experience (QoE) to mobile devices. Unfortunately, exploding numbers of competing devices, potential encroachment by cellular technology, and dramatic increases in content richness deliver a more variable QoE than desired. Moreover, such variance tends to occur both across time and space making it a difficult problem to debug. Existing active approaches tend to be expensive or impractical while existing passive approaches tend to suffer from accuracy issues. In our paper, we propose a novel passive client-side approach that provides an efficient and accurate characterization by taking advantage of the properties of Frame Aggregation (FA) and Block Acknowledgements (BA). We show in the paper that one can accurately derive important metrics such as airtime and throughput with only a minimal amount of observed BAs. We show through extensive experiments the validity of our approach and conduct validation studies in the dense environment of a campus tailgate.
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- Award ID(s):
- 1718405
- NSF-PAR ID:
- 10334585
- Date Published:
- Journal Name:
- International Workshop on Quality of Service (IWQoS)
- Page Range / eLocation ID:
- 1 to 11
- 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/10.1109/IWQOS52092.2021.9521270
