Multi-Tier CloudVR

Mehrabi, Abbas; Siekkinen, Matti; Kämäräinen, Teemu; ̈ski, Antti yl ̈-J ̈

doi:10.1145/3429441

Cited by 35 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, optimizing XR performance by multi-tier computing can be complicated when there are a multitude of computing offloading and caching options to choose. The computing and caching resource coordination between the cloud server and edge servers is studied in [206] and [207]. Based on the information of a static network environment, e.g., transmission rate and XR computing demand, mixed integer nonlinear programming is investigated.…”

Section: A Extended Realitymentioning

confidence: 99%

Toward Immersive Communications in 6G

Shen,

Gao,

et al. 2023

Preprint

View full text Add to dashboard Cite

The sixth generation (6G) networks are expected to enable immersive communications and bridge the physical and the virtual worlds. Integrating extended reality, holography, and haptics, immersive communications will revolutionize how people work, entertain, and communicate by enabling lifelike interactions. However, the unprecedented demand for data transmission rate and the stringent requirements on latency and reliability create challenges for 6G networks to support immersive communications. In this survey article, we present the prospect of immersive communications and investigate emerging solutions to the corresponding challenges for 6G. First, we introduce use cases of immersive communications, in the fields of entertainment, education, and healthcare. Second, we present the concepts of immersive communications, including extended reality, haptic communication, and holographic communication, their basic implementation procedures, and their requirements on networks in terms of transmission rate, latency, and reliability. Third, we summarize the potential solutions to addressing the challenges from the aspects of communication, computing, and networking. Finally, we discuss future research directions and conclude this study.

show abstract

Section: A Extended Realitymentioning

confidence: 99%

Toward Immersive Communications in 6G

Shen,

Gao,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, this study cannot dynamically allocate the available bandwidth and computing resource, and the unified decision of all VR video stream quality by the edge makes it impossible for users to manually adjust the video quality they want. Some studies are oriented to the edge cloud video analysis scenario, and establish a mapping between computing resources and video analysis task accuracy, and jointly optimize video quality and edge computing cluster resource allocation under delay constraints to maximize the average task accuracy 20 . However, this study has some limitations in setting all computing tasks in edge computing cluster, and the model does not consider the difference between the priorities of different tasks and the fairness among multiple users.…”

Section: Related Workmentioning

confidence: 99%

Joint optimization of task scheduling and computing resource allocation for VR video services in 5G‐advanced networks

Huang,

Du,

Yang

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

With the development of 5G networks and related hardware equipment, the demand for various VR video services including panoramic video live broadcast and immersive game is increasing day by day. Due to the explosive growth of data volume and the trend toward lightweight VR equipment, traditional local computing architecture faces problems of insufficient computing power and high cost. As an emerging computing paradigm, the remote computing architecture of cloud‐edge collaboration frees users from cumbersome local restrictions. However, edge computing resources are often limited, and the remote cloud has inconsistent network latency. Therefore, how to adaptively schedule and allocate computing resources is the key to determining VR service quality. Based on an in‐depth analysis of the characteristics of VR video services and network environments, this article first builds a cloud‐edge‐network coorperation computing and transmission architecture for VR video services. This architecture can adaptively offload concurrent computing tasks and allocate computing power to them in VR scenes based on network conditions provided by the 5G‐Advanced networks via network information exposure interface. This paper proposes a general optimization problem to maximize user experience. And a single‐slot delay optimization model was constructed. Combining convex optimization theory and deep learning algorithms, this paper designs a task scheduling and computing power allocation strategy based on deep reinforcement learning. The policy gradually learns optimal task scheduling and resource allocation decisions to maximize delay‐related rewards in the long run. This article further introduces the graph neural network under the attention mechanism to solve the problem of changes in the number of concurrent tasks. Finally, through Cloudsim simulation, this article verifies the superiority of task scheduling and computing power allocation strategies based on deep reinforcement learning compared to other common strategies in terms of resource utilization, client latency, scalability and decision time.

show abstract

“…Such a headset captures the user's head pose and input triggers through sensory devices, allowing real-time generation of synthetic scenes [1]. To create an interactive VR environment that fully immerses users, it is essential to provide a high-quality visual experience and seamless navigation within the virtual world [2]. Conventional VR systems achieve these requirements by utilizing high-end local computers with dedicated graphics cards that boast abundant GPU cores for rendering graphics.…”

Section: Introductionmentioning

confidence: 99%

“…Several recent studies have focused on the optimization of offloading decisions and radio resource allocation for VR offloading. The problem of allocating resources to balance the trade-off among communication, computing, and caching has been studied both under a fixed delay constraint [13] and with the objective of delay minimization [2], [14]. Lin et al [15] solved a similar problem of resource allocation optimization for minimization of the energy consumption of VR headsets.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Foveated Rendering and Offloading in an Edge-Assisted Virtual Reality System

Nyamtiga,

Asiedu,

Hermawan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Foveated rendering (FR)-in which the central foveal layer (the area around the eye gaze) of a virtual reality (VR) image is rendered at the highest resolution and the peripheral layers are rendered at progressively lower resolutions-is an advanced VR technique that controls the balance between computational load and visual quality by adjusting the foveal layer sizes. We consider an edge computingassisted VR computation offloading system incorporating FR and develop a deep reinforcement learning (DRL)-based solution to maximize a unified objective combining the visual quality and the overheads of energy consumption and delay for multiple VR users by optimizing the foveal layer size determination, offloading decisions and radio resource allocation of wireless links with non-orthogonal multiple access (NOMA). To formulate the unified objective, the user-perceived visual quality of a VR image rendered via FR is modeled with the sizes of the foveal layers as variables. The proposed solution consists of three main modules: per-user FR and offloading modules, which determine the sizes of the foveal layers and make offloading decisions, including subband allocation, and a central transmit-power allocation module. The actions of these three modules determine the reward that is in turn fed back to each module. Evaluation results reveal that the proposed framework can better adapt the operations of FR, offloading and wireless transmission to the environmental conditions than other FR and offloading benchmarks in terms of overall reward, visual quality, energy consumption and delay.INDEX TERMS virtual reality, foveated rendering, edge computing, offloading, deep reinforcement learning.

show abstract

Multi-Tier CloudVR

Cited by 35 publications

References 35 publications

Toward Immersive Communications in 6G

Toward Immersive Communications in 6G

Joint optimization of task scheduling and computing resource allocation for VR video services in 5G‐advanced networks

Adaptive Foveated Rendering and Offloading in an Edge-Assisted Virtual Reality System

Contact Info

Product

Resources

About