Performance Analysis of an Adaptive Rate Scheme for QoE-Assured Mobile VR Video Streaming

Chu, Thi My Chinh; Zepernick, Hans‐Jürgen

doi:10.3390/computers11050069

Cited by 6 publications

(7 citation statements)

References 45 publications

(50 reference statements)

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“…Some researches use mobile edge virtualization and caching technology to deal with different wireless link states of users adaptively by providing a variety of modulation and coding schemes at different rates to reduce network load. 15 The third method is to greatly reduce the rotation delay by rendering a viewport larger than the user's viewing angle, and reusing the adjacent areas of the previous frames in the way of intra-frame navigation on the client side. The extra viewport range is positively correlated with network delay and user head rotation speed.…”

Section: Related Workmentioning

confidence: 99%

“…Some studies focus on the inter‐frame similarity problem of multi‐user VR video rendering, by maintaining a VR video frame cache on the edge server, adaptive reuse of redundant VR frames between different users, thus greatly reducing the rendering load. Some researches use mobile edge virtualization and caching technology to deal with different wireless link states of users adaptively by providing a variety of modulation and coding schemes at different rates to reduce network load 15 . The third method is to greatly reduce the rotation delay by rendering a viewport larger than the user's viewing angle, and reusing the adjacent areas of the previous frames in the way of intra‐frame navigation on the client side.…”

Section: Related Workmentioning

confidence: 99%

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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

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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.

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Section: Related Workmentioning

confidence: 99%

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

show abstract

“…In [6], an examination of a network structure tailored for mobile VR video streaming applications is undertaken. The study encompasses various modulation and coding schemes offering different rates, with a subsequent analysis of performance metrics including blocking probability, throughput, queueing delay, and average packet error rate, taking into account Nakagami-m fading on the wireless link.…”

Section: Related Workmentioning

confidence: 99%

“…Nonlinear mapping functions achieve higher correlations compared to the corresponding linear functions [17]. The most frequently utilized mapping functions are shown in the expressions below and include the logistic (2), cubic (3), exponential (4), logarithmic (5) and power function (6):…”

Section: ) Mapping Functionsmentioning

confidence: 99%

Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks

Kougioumtzidis,

Vlahov,

Poulkov

et al. 2023

IEEE Access

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Nowadays, innovative applications in the field of virtual reality (VR) are being developed, attracting the interest of both academia and industry. Wireless VR applications focus on various aspects of daily life, such as smart education, entertainment, healthcare, tourism, architecture, automotive, and industrial automation. All these inherently interactive applications that aim to create immersive experiences for users are closely related to the concept of quality of experience (QoE), which expresses the quality of a service as perceived by end-users. In this paper, we develop an objective QoE prediction model based on deep learning techniques. The prediction model examines the impact of wireless network operation on the quality of VR 360-degree video streaming. It is based on an encoder-decoder long short-term memory (LSTM) neural network and is able to predict in real-time the overall transmission-related QoE value using only measurable quality of service (QoS) parameters. The prediction model is tested and evaluated on an open radio access network testbed with interfaces based on the O-RAN specifications. INDEX TERMSDeep learning, deep neural network (DNN), long short-term memory (LSTM), open radio access network (Open RAN), quality of experience (QoE), QoE prediction, recurrent neural network (RNN), virtual reality (VR).

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“…The research [35] discusses the advantages of millimeter wave in VR video transmission and proposes a solution using mobile edge computing as well as pre-caching, aiming to provide reliable VR video transmission, taking the more interactive VR games as the research object. In the research [36], the problem of fusing millimeter wave communication, Given above analysis and description, we summarize main contributions of this paper as follows:…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning-Based Optimal Computation Offloading Scheme for VR Video Transmission in Mobile Edge Networks

Xu,

Song

2023

IEEE Access

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Large bandwidth, Low latency and intensive computing are the main challenge in high-performance virtual reality (VR) video transmission. As mobile edge computing (MEC) can provide computation and storage resources closer to terminals, it has been a promising mode in VR video transmission to substantially improve communication quality. This work focuses on the autonomous perception ability in MEC-supported VR video transmission, and introduces deep reinforcement learning to investigate optimal task offloading solutions. Therefore, this paper proposes a deep reinforcement learning-based optimal computation offloading scheme for VR video transmission in mobile edge networks. Specifically, a Deep Deterministic Policy Gradient-based computation offloading algorithm in designed as the main technical framework. The optimal planning of computation offloading strategies is viewed as a Markov decision problem, and a deep Q-Network is employed to deal with it. Finally, the setting of MEC-supported VR video transmission scenes is simulated, in which the proposed scheme is implemented for evaluation. The results are displayed in visualization format and show that the proposed task computation scheme can possess proper performance results in MEC-supported VR video transmission scenes.INDEX TERMS Virtual reality, video transmission, computation offloading, mobile edge networks, reinforcement learning.

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Performance Analysis of an Adaptive Rate Scheme for QoE-Assured Mobile VR Video Streaming

Cited by 6 publications

References 45 publications

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

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

Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks

A Deep Reinforcement Learning-Based Optimal Computation Offloading Scheme for VR Video Transmission in Mobile Edge Networks

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