The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
LiveObj: Object Semantics-based Viewport Prediction for Live Mobile Virtual Reality Streaming
Abstract
Virtual reality (VR) video streaming (a.k.a., 360-degree video streaming) has been gaining popularity recently as a new form of multimedia providing the users with immersive viewing experience. However, the high volume of data for the 360-degree video frames creates significant bandwidth challenges. Research efforts have been made to reduce the bandwidth consumption by predicting and selectively streaming the user's viewports. However, the existing approaches require historical user or video data and cannot be applied to live streaming, the most attractive VR streaming scenario. We develop a live viewport prediction mechanism, namely LiveObj, by detecting the objects in the video based on their semantics. The detected objects are then tracked to infer the user's viewport in real time by employing a reinforcement learning algorithm. Our evaluations based on 48 users watching 10 VR videos demonstrate high prediction accuracy and significant bandwidth savings obtained by LiveObj. Also, LiveObj achieves real-time performance with low processing delays, meeting the requirement of live VR streaming.
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“By adopting the idea that encoding expensive-toevaluate elements into constraints is robust to changing environments [21][26], we propose to characterize the scheduling problem as a constrained RL problem by constructing a reward function with easy-to-evaluate metrics and a constraint with unknown and expensive-to-evaluate users' requirements.”
From Orchestrating Joint Offloading and Scheduling for Low-Latency Edge SLAM (2025) · cites this paper as [26]
Citations
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Cited by
Sorted by citations
NetLLM: adapting large language models for networking
Wu, D., Wang, X., Qiao, Y., Wang, Z., Jiang, J., Cui, S., & Wang, F. (2024) · Proceedings of the ACM SIGCOMM 2024 Conference · doi:10.1145/3651890.3672268 · Cited by 167
“By default, we train and test all methods to stream the Envivio-Dash3 video from the DASH-246 JavaScript reference client [25], whose format follows the GENET [103] and Pensieve [62] setting.”
BackgroundMethodInfluentialOrchestrating Joint Offloading and Scheduling for Low-Latency Edge SLAM
Zhang, Y., Mao, Y., Wang, H., Yu, Z., Guo, S., Zhang, J., Wang, L., & Guo, B. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3547256 · Cited by 1
“To enable accurate and real-time tile-oriented importance evaluation, we develop a regional feature prediction model by adopting the principle of lifelong learning [27], [28].”
Metadata
- Authors
- Feng, X., Bao, Z., & Wei, S.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2021-05
- Volume
- 27
- Issue
- 5
- Pages
- 2736-2745
- ISSN
- 1077-2626 (ISSN-L)
- 1941-0506
- 2160-9306
- OpenAlex
- W3141438256
- Cited by
- 34
- References
- 1
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 6 · 2023: 9 · 2022: 7 · 2021: 1
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (CNS-1910085)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Physical Sciences › Engineering › Media Technology › Multimedia Communication and Technology
- Keywords
- 360-degree video, viewport prediction, object semantics, object detection, object tracking, reinforcement learning, bandwidth reduction, mobile virtual reality, live streaming
- MeSH terms
- Virtual Reality, Humans, Image Processing, Computer-Assisted, Semantics, Video Recording
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- CoChat. (2026). Citation check: LiveObj: Object Semantics-based Viewport Prediction for Live Mobile Virtual Reality Streaming. https://app.cochat.ai/papers/10.1109/tvcg.2021.3067686
- The paper
- Feng, X., Bao, Z., & Wei, S. (2021). LiveObj: Object Semantics-based Viewport Prediction for Live Mobile Virtual Reality Streaming. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/tvcg.2021.3067686
