Towards 6DoF HTTP Adaptive Streaming Through Point Cloud Compression

Hooft, Jeroen van der; Wauters, Tim; Turck, Filip De; Timmerer, Christian; Hellwagner, Hermann

doi:10.1145/3343031.3350917

Cited by 85 publications

(77 citation statements)

References 11 publications

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“…Point cloud video streaming has drawn increasing attention recently [10], [11], [12], [13], [14], [15]. Most of these works are based on VR/360 video streaming schemes [1], [19], i.e., a point cloud video is divided into smaller tiles, and only tiles inside the user's field of view (FoV) (denotes the part of the video the user is watching) are transmitted, and optimization can be conducted following this thread to optimize the defined objective function [10], [11]. However, these works do not consider the decoding complexity [12], [13], which makes them hard to deploy in real systems.…”

Section: B Point Cloud Video Streamingmentioning

confidence: 99%

“…These factors make point cloud video streaming very different from traditional video streaming [8], [9] and should be considered in transmission scheme design. This paper focuses on point cloud video streaming, which has recently become a research hotspot [10], [11], [12], [13], [14], [15] and aims to solve the inherent technical issues to provide users with higher video quality.…”

Section: Introductionmentioning

confidence: 99%

“…Different tiles have different quality levels, and the quality levels are selected depending on their relation to the user's view frustum and distance to the user [10]. [11] presented a number of rate adaptation heuristics that use information on the user's position and focus, the available bandwidth, and the client's buffer status to determine the most appropriate quality representation of each object. However, these works did not consider the decoding complexity [12], [13], i.e., the computational requirement of the existing point cloud video codec for decoding exceeds the computational capability of user devices.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

Li¹,

Chen²,

Wu³

et al. 2020

Preprint

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Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned into smaller tiles, along with a low-quality base layer covering the entire video. Each tile is encoded into different quality levels, and both the compressed and uncompressed (i.e., decoded) versions of each tile are prepared for selection. Then, based on the user’s viewing angle and predicted future network bandwidth condition, fuzzy logic empowered quality level selection, with properly defined novel fuzzification, fuzzy rules, and defuzzification, is conducted to maximize the received point cloud video quality under the communication resource, computational resource and quality requirements constraints. Extensive simulations based on real point cloud video sequences and network traces are conducted, and the results reveal the superiority of the proposed scheme over the baseline scheme. To the best of our knowledge, this is the first work studying point cloud video streaming using fuzzy logic. <br>

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Section: B Point Cloud Video Streamingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

Li¹,

Chen²,

Wu³

et al. 2020

Preprint

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show abstract

“…A i represents the area of the user's FOV occupied by the object i, equal to 1 when the object occupies the entire FOV. This value depends both on the distance and the physical size of the object and is computed as the area of the convex hull of the object bounding box, as also proposed by van der Hooft et al [15]. Despite being an approximation of the real percentage of FOV occupied by the object, A i nevertheless indicates which objects are bigger and/or closer to the user, and therefore sets priority on objects with regards to their impact on user's visual perception.Ã i is the potential area of object i in the FOV, i.e., the area that would result if the user would turn and point directly towards i.…”

Section: A Object Prioritymentioning

confidence: 99%

Dynamic Adaptive Streaming for Augmented Reality Applications

Petrangeli

Simon

Wang

et al. 2019

2019 IEEE International Symposium on Multimedia (ISM)

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Augmented Reality (AR) superimposes digital content on top of the real world, to enhance it and provide a new generation of media experiences. To provide a realistic AR experience, objects in the scene should be delivered with both high photorealism and low latency. Current AR experiences are mostly delivered with a download-and-play strategy, where the whole scene is considered a monolithic entity for delivery. This approach results in high start-up latencies and therefore a poor user experience. A similar problem in the video domain has already been tackled with the HTTP Adaptive Streaming (HAS) principle, where the video is split into segments, and a rate adaptation heuristic dynamically adapts the video quality based on the available network resources. In this paper, we apply the adaptive streaming principle from the video to the AR domain, and propose a streaming framework for AR applications. In our proposed framework, the AR objects are available at different Levels-Of-Detail (LODs) and can be streamed independently from each other. An LOD adaptation heuristic is in charge of dynamically deciding what object should be fetched from the server and at what LOD level. Our proposed heuristic prioritizes content that is more likely to be viewed by the user and selects the best LOD to maximize the object's perceived visual quality. Moreover, the adaptation takes into account the available bandwidth resources to ensure a timely delivery of the AR objects. Experiments carried out over the Internet using an AR streaming prototype developed on an iOS device allow us to show the gains brought by the proposed framework. Particularly, our approach can decrease start-up latency up to 90% with respect to a download-and-play baseline, and decrease the amount of data needed to deliver the AR experience up to 79%, without sacrificing on the visual quality of the AR objects.

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“…Even though various approaches have already proposed to apply HAS to point clouds, this is still a niche area of research. Notable studies include the ones by Hosseini and Timmerer [1] and van der Hooft et al [2]. While these studies agree on the advantages of using HAS to nicely trade off the streaming quality with the bandwidth consumption, the impact of this trade-off on the user perception, i.e., on the quality of experience (QoE), is yet to be thoroughly examined.…”

Section: Introductionmentioning

confidence: 99%

Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming

Hooft

Vega

Timmerer

et al. 2020

2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)

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Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide today. Therefore, recent efforts have started to target efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis, we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned.

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Towards 6DoF HTTP Adaptive Streaming Through Point Cloud Compression

Cited by 85 publications

References 11 publications

Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

Dynamic Adaptive Streaming for Augmented Reality Applications

Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming

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