Tile-Based Qoe-Driven Http/2 Streaming System For 360 Video

Xu, Zhimin; Ban, Yixuan; Zhang, Kai; Xie, Linzhen; Zhang, Xinggong; Guo, Zongming; Meng, Shengbin; Wang, Yue

doi:10.1109/icmew.2018.8551493

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Xu et al [256] employed a k-push scheme for 360 • videos to push k number of tiles to the client that compose a single temporal segment. The proposed method along with the QoEaware bitrate adaptation algorithm improves the video quality by 20% and reduces the network transmission delay by up to 30%, under different RTT settings.…”

Section: Low Latency Streamingmentioning

confidence: 99%

“…Edge-assisted solutions are predominant in taming the latency in single- [21], [48] and multi-user [261], [262] environments. Besides, the support for server-based viewport computation [254], server-push mechanisms [186], [256], [257], and remote rendering [21], [259] also enable low latency streaming over current wireless networks. The current 4G networks are enough for early adopter immersive multimedia.…”

Section: Low Latency Streamingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

Yaqoob

Muntean

2020

IEEE Commun. Surv. Tutorials

146

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Omnidirectional or 360 • video is increasingly being used, mostly due to the latest advancements in immersive Virtual Reality (VR) technology. However, its wide adoption is hindered by the higher bandwidth and lower latency requirements than associated with traditional video content delivery. Diverse researchers propose and design solutions that help support an immersive visual experience of 360 • video, primarily when delivered over a dynamic network environment. This paper presents the state-of-the-art on adaptive 360 • video delivery solutions considering end-to-end video streaming in general and then specifically of 360 • video delivery. Current and emerging solutions for adaptive 360 • video streaming, including viewport-independent, viewport-dependent, and tile-based schemes are presented. Next, solutions for network-assisted unicast and multicast streaming of 360 • video content are discussed. Different research challenges for both on-demand and live 360 • video streaming are also analyzed. Several proposed standards and technologies and top international research projects are then presented. We demonstrate the ongoing standardization efforts for 360 • media services that ensure interoperability and immersive media deployment on a massive scale. Finally, the paper concludes with a discussion about future research opportunities enabled by 360 • video.

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Section: Low Latency Streamingmentioning

confidence: 99%

Section: Low Latency Streamingmentioning

confidence: 99%

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

Yaqoob

Muntean

2020

IEEE Commun. Surv. Tutorials

146

View full text Add to dashboard Cite

show abstract

“…Another work [257] mixing the two prediction methods uses a linear combination of the two outputs, considering the trade-off between the flexibility of the adaptation and the coding efficiency, which decreases as the number of tiles grows. A k-Nearest Neighbors (k-NN) was exploited in [258] to make use of previous users' data by finding similar scanpaths and assigning future FoVs from those users a larger probability.…”

Section: Viewport-dependent Streamingmentioning

confidence: 99%

A Survey on 360-Degree Video: Coding, Quality of Experience and Streaming

Chiariotti¹

2021

Preprint

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The commercialization of Virtual Reality (VR) headsets has made immersive and 360 • video streaming the subject of intense interest in the industry and research communities. While the basic principles of video streaming are the same, immersive video presents a set of specific challenges that need to be addressed. In this survey, we present the latest developments in the relevant literature on four of the most important ones: (i) omnidirectional video coding and compression, (ii) subjective and objective Quality of Experience (QoE) and the factors that can affect it, (iii) saliency measurement and Field of View (FoV) prediction, and (iv) the adaptive streaming of immersive 360 • videos.The final objective of the survey is to provide an overview of the research on all the elements of an immersive video streaming system, giving the reader an understanding of their interplay and performance.

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“…Several prior studies also exploited the cross-users behaviors instead of only the target user's historical trajectories. [6] and [7] combined a linear regression (LR) model with KNN clustering. From historical trajectories of head movements, FoV center is firstly predicted using a linear regression model, then the K nearest fixations of other users around the LR result are found to improve the prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Very Long Term Field of View Prediction for 360-Degree Video Streaming

Zhang

Liu

et al. 2019

2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

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360-degree videos have gained increasing popularity in recent years with the developments and advances in Virtual Reality (VR) and Augmented Reality (AR) technologies. In such applications, a user only watches a video scene within a field of view (FoV) centered in a certain direction. Predicting the future FoV in a long time horizon (more than seconds ahead) can help save bandwidth resources in on-demand video streaming while minimizing video freezing in networks with significant bandwidth variations. In this work, we treat the FoV prediction as a sequence learning problem, and propose to predict the target user's future FoV not only based on the user's own past FoV center trajectory but also other users' future FoV locations. We propose multiple prediction models based on two different FoV representations: one using FoV center trajectories and another using equirectangular heatmaps that represent the FoV center distributions. Extensive evaluations with two public datasets demonstrate that the proposed models can significantly outperform benchmark models, and other users' FoVs are very helpful for improving long-term predictions.Index Terms-virtual reality; 360-degree video streaming; time series prediction; field of view;

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Tile-Based Qoe-Driven Http/2 Streaming System For 360 Video

Cited by 7 publications

References 9 publications

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

A Survey on 360-Degree Video: Coding, Quality of Experience and Streaming

Very Long Term Field of View Prediction for 360-Degree Video Streaming

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