Demo Abstract: Narwhal: a DASH-based Point Cloud Video Streaming System over Wireless Networks

Li, Jie; Zhang, Cong; Liu, Zhi; Sun, Wei; Hu, Wei; Li, Qiyue

doi:10.1109/infocomwkshps50562.2020.9162937

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…In addition, due to the huge amount of point cloud data, current volumetric video streaming systems generally compress the video first before transmission, and then the clients can decode, render and play on their devices. However, the transmission time affected by different network bandwidths, as well as decoding time affected by different computational resources and decoder may cause playback to stall, which has a negative impact on QoE [17] , [4] . Finally, due to the adaptive mechanism of streaming system, users will see video contents of different quality representations, the quality switch of video content seen by users will also impair QoE [25] .…”

Section: A Overviewmentioning

confidence: 99%

“…Experimental apparatus and environment: We use ITU-R BT.500-14 4 as the reference standards of our subjective experiments. We design a point cloud video player based on Unity, ask subjects wear the HTC vive HMD to enjoy the generated point cloud video, as shown in the Fig.…”

Section: Subjective Experimentsmentioning

confidence: 99%

“…Specifically, we train the first-time subjects by watching point cloud videos of different contents and qualities, while point cloud video experts are not trained. After a short rest, all subjects evaluate the received point cloud videos using the double-stimulus impairment scale (DSIS) 4 method.…”

Section: Subjective Experimentsmentioning

confidence: 99%

“…MB, which means the point cloud video streaming bandwidth demand is up to 2.9 Gbps [4] . This requirement will be even higher when delivering high-quality video.…”

Section: Introductionmentioning

confidence: 99%

“…As far as we know, this is the first work to study the QoE model for point cloud video streaming. The contributions of this article are summarized as follows: 4) We made public the subjective experimental MOS data, which may stimulate more research on this area. The rest of this article is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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A QoE Model in Point Cloud Video Streaming

Li¹,

Wang²,

Li³

et al. 2021

Preprint

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Point cloud video has been widely used by augmented reality (AR) and virtual reality (VR) applications as it allows users to have an immersive experience of six degrees of freedom (6DoFs). Yet there is still a lack of research on quality of experience (QoE) model of point cloud video streaming, which cannot provide optimization metric for streaming systems. Besides, position and color information contained in each pixel of point cloud video, and viewport distance effect caused by 6DoFs viewing procedure make the traditional objective quality evaluation metric cannot be directly used in point cloud video streaming system. In this paper we first analyze the subjective and objective factors related to QoE model. Then an experimental system to simulate point cloud video streaming is setup and detailed subjective quality evaluation experiments are carried out. Based on collected mean opinion score (MOS) data, we propose a QoE model for point cloud video streaming. We also verify the model by actual subjective scoring, and the results show that the proposed QoE model can accurately reflect users' visual perception. We also make the experimental database public to promote the QoE research of point cloud video streaming.

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Section: A Overviewmentioning

confidence: 99%

Section: Subjective Experimentsmentioning

confidence: 99%

Section: Subjective Experimentsmentioning

confidence: 99%

“…MB, which means the point cloud video streaming bandwidth demand is up to 2.9 Gbps [4] . This requirement will be even higher when delivering high-quality video.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A QoE Model in Point Cloud Video Streaming

Li¹,

Wang²,

Li³

et al. 2021

Preprint

Self Cite

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Emulation of point cloud streaming over 5G network

Makiyah,

Khamees

2024

Int. j. inf. tecnol.

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Realistic digital representations of 3D objects and environments are now achievable because to recent developments in computer graphics, enabling realtime user interactions. Creating effective compression techniques and technologies that may take into account varied application limits has become a crucial problem due to the rising need for various point clouds. Future wireless networks are expected to undergo a paradigm change as a result of the 3GPP's 5G Advanced development. In this paper, we propose a complete system for streaming 3D high density point cloud data using a web-based streaming server with HTTP/2 protocol enabled, and compare results in two scenarios over WIFI and over 5G standalone network. Results have shown great outperformance over conventional work by decreasing inter-frame latency for large point cloud streaming by 16.8% for the case of 4 million points using http/2 over wi , and by 71.51% over emulated 5G network. Streamed packets were also captured showing an increased frame rate of the same sample by 20.7% and 353% for the cases of wi and 5G networks, respectively.

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Point Cloud Streaming over HTTP/2

Makiyah,

Kliamees

2023

2023 6th International Conference on Advanced Communication Technologies and Networking (CommNet)

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Demo Abstract: Narwhal: a DASH-based Point Cloud Video Streaming System over Wireless Networks

Cited by 8 publications

References 3 publications

A QoE Model in Point Cloud Video Streaming

A QoE Model in Point Cloud Video Streaming

Emulation of point cloud streaming over 5G network

Point Cloud Streaming over HTTP/2

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