Joint Rate and FoV adaptation in immersive video streaming

He, Dongbiao; Westphal, Cédric; Garcia-Luna-Aceves, J.J.

doi:10.1145/3229625.3229630

Cited by 32 publications

(21 citation statements)

References 13 publications

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“…Instead of solely relying on viewport and bandwidth data for quality adjustments, Nguyen et al [58] proposed to select the viewport bitrate by also taking into account the viewport prediction errors during each segment duration. He et al [59] performed a network delay-based joint selection of the viewport coverage and bitrate. The simulation outcomes confirm that adaptable viewport coverage offers improved quality streaming under different delay settings.…”

Section: B Tile-based Adaptive Streamingmentioning

confidence: 99%

A Combined Field-of-View Prediction-Assisted Viewport Adaptive Delivery Scheme for 360° Videos

Yaqoob

Muntean

2021

IEEE Trans. on Broadcast.

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Recently, 360 • or omnidirectional videos have become increasingly popular for both personal and enterprize use-cases. However, 360 • video streaming has very high bandwidth and processing requirements. State-of-the-art viewport-based streaming solutions lower these requirements by performing selective streaming based on long-term Field-of-View (FoV) prediction mechanisms. However, sometimes user movement is extremely unpredictable during some parts of the video, and applying these solutions adversely affects the overall quality of experience (QoE). This paper proposes a novel Combined Field-of-View tile-based adaptive streaming solution (CFOV) that improves end-user QoE for 360 • video streaming. CFOV performs interactive tile selection based on more accurate dynamical viewing area identification by combining the results of two FoV prediction mechanisms. It also employs an innovative priority-based bitrate adaptation approach that ensures improved bitrate budget distribution between different tiles. We evaluate the proposed solution with a comprehensive set of experiments involving four immersive videos, diverse tiling patterns (i.e., 4x3, 6x4, and 8x6), different segment lengths (i.e., 1s, 2s, and 3s), and 48 empirical head movement traces under different bandwidth settings. The evaluation employs a newly defined QoE metric specifically introduced to assess the streaming performance of 360 • videos objectively. The experimental findings show that, compared to alternative approaches, our proposed solution can achieve a higher viewport match and can significantly improve the user QoE for different watching behaviors and content characteristics.

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Section: B Tile-based Adaptive Streamingmentioning

confidence: 99%

A Combined Field-of-View Prediction-Assisted Viewport Adaptive Delivery Scheme for 360° Videos

Yaqoob

Muntean

2021

IEEE Trans. on Broadcast.

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“…Other works use prediction mechanism to perform rate adaption for network distribution. For instance, He et al [12] propose a joint rate and FoV adaptation that varies the anticipation window based upon the network response.…”

Section: A User Behavior Analysis For Immersive Videosmentioning

confidence: 99%

Towards Tile Based Distribution Simulation in Immersive Video Streaming

He¹,

Westphal²,

Jiang³

et al. 2019

2019 IFIP Networking Conference (IFIP Networking)

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There has been increasing attention to virtual reality applications in recent years, especially to immersive or 360degree videos that typically consume much more bandwidth than traditional ones. Though all produced data is transferred, only a small part (denoted as Field of View or viewport) is watched by users due to the nature of immersive videos. Obviously, this causes a large waste of network resources. Hence, it is important to define a viewport-dependent streaming transmission strategy by detecting where the user is gazing and the movement of the user's head. Unfortunately, there are few datasets providing this information. In this paper, we propose a tile-based simulation approach to generate the distribution of the user's behavior and to provide information that can be used to optimize future viewdependent streaming protocols. We first characterize the users' viewport pattern from datasets gathered from real users by decomposing the 360-degree stream into tiles and analyzing the frequency and time-interval distribution for each tile. Then, we devise a hierarchical Markov model that incorporates the beta distribution of each tile time interval to predict tile transition. The results show that the simulation tool characterizes the tile sequences of users accurately, performing close to the empirical results.Index Terms-AR/VR, 360-degree video, Markov Model, User behavior, Simulation

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“…He et al (He et al, 2018) looked at the joint adaptation of the field of view with the rate under variations of the network delay and the congestion. They proposed an algorithm to adapt the size of the field of view to be downloaded based upon the network and buffering delay to retrieve and view the stream segment so that it encompasses with high likelihood the user's viewport at the time of rendering.…”

Section: Viewport-dependent Streamingmentioning

confidence: 99%

Network Support for AR/VR and Immersive Video Application: A Survey

Westphal

Garcia-Luna-Aceves

2018

Proceedings of the 15th International Joint Conference on E-Business and Telecommunications

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Abstract:Augmented Reality and Virtual Reality are rapidly gaining attention and are increasingly being deployed over the network. These technologies have large industrial potential to become next big platform with a wide range of applications. This experience will only be satisfying when the network infrastructure is able to support these applications. Current networks however, are still having a hard time streaming high quality videos. The advent of 5G Networks will improve the network performance, but it is unclear it will be sufficient to provide new applications delivering augmented reality and virtual reality services. There are few surveys on the topic of augmented reality systems, and their focus mostly stays on the actual displays and potential applications. We survey the literature on AR/VR networking, and we focus here on the potential underlying network issues.

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Joint Rate and FoV adaptation in immersive video streaming

Cited by 32 publications

References 13 publications

A Combined Field-of-View Prediction-Assisted Viewport Adaptive Delivery Scheme for 360° Videos

A Combined Field-of-View Prediction-Assisted Viewport Adaptive Delivery Scheme for 360° Videos

Towards Tile Based Distribution Simulation in Immersive Video Streaming

Network Support for AR/VR and Immersive Video Application: A Survey

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