Collaborative Edge Caching and Transcoding for 360° Video Streaming Based on Deep Reinforcement Learning

Yang, Taoyu; Tan, Zengjie; Xu, Yuanyuan

doi:10.1109/jiot.2022.3197798

Cited by 17 publications

(3 citation statements)

References 44 publications

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“…Moreover, similar schemes using ABR algorithms to improve the delivery of VR video have also been proposed. [15][16][17][18] Besides, for 360 • video streams, considering the cooperative edge transcoding and caching problem in edge clusters, Yang et al 19 proposed a deep reinforcement learning-based stream caching and computing resource allocation scheme. Storage and computation resources are allocated jointly to improve cache hit rate and reduce latency and transmission cost.…”

Section: • Vr Video Streamingmentioning

confidence: 99%

OJUMP: Optimization for joint unicast‐multicast panoramic VR live streaming system

Pan,

Zeng,

Zhuang

et al. 2023

Trans Emerging Tel Tech

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Virtual reality (VR) videos have garnered increasing attention due to their ability to deliver immersive multimedia experiences to users. Nonetheless, the demanding requirements of high bandwidth and low latency for VR services present significant challenges, especially in the context of network infrastructure. In this article, we introduce a novel system called OJUMP, aimed at Optimizing the transmission and distribution of Joint Unicast‐Multicast Panoramic video field of view live streaming using Bit Index Explicit Replication (BIER). The proposed system encompasses a BIER‐based network transmission approach that enhances video transmission efficiency and facilitates large‐scale service deployment. Furthermore, OJUMP includes a dynamic disk‐state‐based tile slice distribution storage algorithm and a prefetch method designed for edge nodes to enhance the distribution efficiency of live streaming services. Experimental results illustrate that the proposed system offers distinct advantages over the baseline system in terms of bandwidth, latency, disk utilization, and central processing unit utilization.

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Section: • Vr Video Streamingmentioning

confidence: 99%

OJUMP: Optimization for joint unicast‐multicast panoramic VR live streaming system

Pan,

Zeng,

Zhuang

et al. 2023

Trans Emerging Tel Tech

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show abstract

“…Trans-coding is usually applied in 360 streaming systems. [13] leverage a DRL-based model to make caching and transcoding decisions to reduce delay, transmission cost and quality mismatch level with limited cache size and computational power. In [14], multi-agent RL is proposed for a transcoding-enable edge caching model to minimize communication and transcoding latency with a constrained transcoding power.…”

Section: Related Workmentioning

confidence: 99%

QoE-Driven Coupled Uplink and Downlink Rate Adaptation for 360-Degree Video Live Streaming

Feng

Sun

et al. 2020

IEEE Commun. Lett.

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360-degree video provides an immersive 360-degree viewing experience and has been widely used in many areas. The 360-degree video live streaming systems involve capturing, compression, uplink (camera to video server) and downlink (video server to user) transmissions. However, few studies have jointly investigated such complex systems, especially the rate adaptation for the coupled uplink and downlink in the 360degree video streaming under limited bandwidth constraints. In this letter, we propose a quality of experience (QoE)-driven 360-degree video live streaming system, in which a video server performs rate adaptation based on the uplink and downlink bandwidths and information concerning each user's real-time field-of-view (FOV). We formulate it as a nonlinear integer programming problem and propose an algorithm, which combines the Karush-Kuhn-Tucker (KKT) condition and branch and bound method, to solve it. The numerical results show that the proposed optimization model can improve users' QoE significantly in comparison with other baseline schemes.

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“…To avoid the transmission redundancy associated with repeated requests and reduce the traffic load on the backhaul path, some works [9], [10], [11] bring popular content closer to users by deploying transcoding-enabled cache servers at the edge of the Content Delivery Network (CDN). The application of cache servers effectively reduces the load on the content servers and improves users' QoE on the client side.…”

Section: Introductionmentioning

confidence: 99%

VRCT: A Viewport Reconstruction-Based 360° Video Caching Solution for Tile-Adaptive Streaming

et al. 2023

IEEE Trans. on Broadcast.

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video streaming demands higher bandwidth and lower latency than conventional videos. Some solutions employ tile-adaptive 360 • video streaming and edge caching mechanisms to improve the quality of their content delivery. However, it is hard to cache large amounts of popular content with limited cache capacity. Reconstruction technologies, which have been widely adopted for images and conventional videos, can potentially reconstruct complete tile-based viewports from partial observation for 360 • videos, thus further relieving the pressure on the caching. However, it is challenging to design a flexible and efficient caching solution that supports viewport reconstruction for 360 • videos. In this paper, we propose a Viewport Reconstructionbased 360 • video Caching solution for Tile-adaptive streaming (VRCT). To enhance viewers' quality of experience (QoE), a QoE-driven reconstruction trigger scheme is designed to determine whether to perform reconstruction or not based on current cache information and network conditions. To make efficient use of the cache space and facilitate the viewport reconstruction, a heuristic-based solution, named aggregation-based cache replacement scheme, is proposed to improve the probability of viewport reconstruction by carefully selecting which tiles to be stored in the given limited space. Through comprehensive experiments with a real head movement dataset, we show that the proposed VRCT

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Collaborative Edge Caching and Transcoding for 360° Video Streaming Based on Deep Reinforcement Learning

Cited by 17 publications

References 44 publications

OJUMP: Optimization for joint unicast‐multicast panoramic VR live streaming system

OJUMP: Optimization for joint unicast‐multicast panoramic VR live streaming system

QoE-Driven Coupled Uplink and Downlink Rate Adaptation for 360-Degree Video Live Streaming

VRCT: A Viewport Reconstruction-Based 360° Video Caching Solution for Tile-Adaptive Streaming

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