A survey on 360-degree video: Coding, quality of experience and streaming

Chiariotti, Federico

doi:10.1016/j.comcom.2021.06.029

Cited by 53 publications

(16 citation statements)

References 98 publications

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“…However, most works focused specifically on XR still consider simple applications, such as interactive data visualization [21], and do not provide much insight on the more complex scenarios. There is an extensive literature on immersive video streaming [22], but it has been mostly focused on passive applications in which the user is only a viewer, with different QoE and encoding considerations.…”

Section: State Of the Artmentioning

confidence: 99%

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Lecci¹,

Chiariotti²,

Drago³

et al. 2022

Preprint

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Over the past few years, eXtended Reality (XR) has attracted increasing interest thanks to its extensive industrial and commercial applications, and its popularity is expected to rise exponentially over the next decade. However, the stringent Quality of Service (QoS) constraints imposed by XR's interactive nature require Network Slicing (NS) solutions to support its use over wireless connections: in this context, quasi-Constant Bit Rate (CBR) encoding is a promising solution, as it can increase the predictability of the stream, making the network resource allocation easier. However, traffic characterization of XR streams is still a largely unexplored subject, particularly with this encoding. In this work, we characterize XR streams from more than 4 hours of traces captured in a real setup, analyzing their temporal correlation and proposing two prediction models for future frame size. Our results show that even the state-of-the-art H.264 CBR mode can have significant frame size fluctuations, which can impact the NS optimization. Our proposed prediction models can be applied to different traces, and even to different contents, achieving very similar performance. We also show the trade-off between network resource efficiency and XR QoS in a simple NS use case.

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Section: State Of the Artmentioning

confidence: 99%

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Lecci¹,

Chiariotti²,

Drago³

et al. 2022

Preprint

Self Cite

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“…Tile-based adaptive streaming has been widely investigated in omnidirectional content and more recently also for volumetric content [ 115 , 116 ]. We remind the reader of several excellent review papers on adaptive streaming for immersive communications [ 10 , 11 , 117 ]. In the following, we mainly focus on the recent advances that made usage of machine learning tools.…”

Section: Learning-based Transmissionmentioning

confidence: 99%

Machine Learning for Multimedia Communications

Thomos

Maugey

Toni

2022

Sensors

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Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.

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“…From a more technical perspective, a complete overview of the latest developments on immersive and 360 • video streaming can be found in [11], where the author aims at providing a complete overview on four of the most important challenges in this field, namely: omnidirectional video coding and compression, subjective and objective QoE and the factors that can affect it, saliency measurement and Field-of-View (FoV) prediction, and adaptive streaming of immersive 360 • videos. As stressed in [11], finding a proper way to measure the user's QoE may be difficult. This is especially important with respect to the design of telecommunication infrastructures able to optimize the experience of the user, and to guarantee constant and stable service quality.…”

Section: State Of the Artmentioning

confidence: 99%

“…In this paper, we will focus on examples related to the video gaming world (even though equivalent conclusions can be drawn for different XR applications), where the user interacts with the application using a keyboard or joypad, and the results of such actions are immediately seen on the PC or TV screen. Through Head Mounted Devices (HMDs), when playing with videogames supporting VR, users can also react by moving their heads and, for instance, depending on where the head is pointing, the application streams distinct portions of the environment [11].…”

Section: Introductionmentioning

confidence: 99%

An Open Framework for Analyzing and Modeling XR Network Traffic

Lecci¹,

Drago²,

Zanella³

et al. 2021

Preprint

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The identification of any commercial product or trade name does not imply endorsement or recommendation by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose. Mattia Lecci's activities were also supported by Fondazione CaRiPaRo under the grant "Dottorati di Ricerca 2018." A preliminary version of this paper proposing a simpler traffic model with fewer traffic traces was presented in [1].

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A survey on 360-degree video: Coding, quality of experience and streaming

Cited by 53 publications

References 98 publications

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Machine Learning for Multimedia Communications

An Open Framework for Analyzing and Modeling XR Network Traffic

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