Deep reinforcement learning-driven intelligent panoramic video bitrate adaptation

Xiao, Gongwei; Xu, Chen; Wu, Muhong; Zhou, Zhi

doi:10.1145/3321408.3321603

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Plato [264] is another system that assumes an external prediction as input to a DRL system, in this case performed by an LSTM. A similar solution was presented in [265], modeling buffer overflows explicitly. Another work using DRL [266] performs the FoV prediction implicitly, using an LSTM to keep track of the historical trends in capacity and viewport orientation.…”

Section: Viewport-dependent Streamingmentioning

confidence: 98%

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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Section: Viewport-dependent Streamingmentioning

confidence: 98%

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

Chiariotti¹

2021

Preprint

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“…Deep learning enables RL to optimize an aggregated reward further using multi-faceted state and action spaces [138]. Kan et al [139] and Xiao et al [140] designed a deep reinforcement learning (DRL) framework that adaptively adjusts the streaming policy based on exploration and exploitation of environmental factors. Both solutions perform the bitrate decision with the A3C algorithm of DRL due to its effectiveness in making agents more and more intelligent.…”

Section: Tile-based Streamingmentioning

confidence: 99%

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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“…DRL360 [41] adaptively allocated rates for the tiles of the future video frames based on the observations collected by client video players. [76] determined the quality level of the tiles in estimated FoV. [77] determined the bitrates for multiple estimated FoVs.…”

Section: A Bitrate Adaption Designmentioning

confidence: 99%

Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT

Luo

Chen

et al. 2021

Preprint

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Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement. Compared to traditional video streaming, VR is with ultra high-definition and dynamically changes with users' head and eye movements, which poses significant challenges for the realization of such potential. In this paper, we provide a detailed and systematic survey of enabling technologies of virtual reality and its applications in Internet of Things (IoT). We identify major challenges of virtual reality on system design, view prediction, computation, streaming, and quality of experience evaluation. We discuss each of them by extensively surveying and reviewing related papers in the recent years. We also introduce several use cases of VR for IoT. Last, issues and future research directions are also identified and discussed.

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Deep reinforcement learning-driven intelligent panoramic video bitrate adaptation

Cited by 6 publications

References 7 publications

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

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

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

Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT

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