Enhancing Video Streaming in Vehicular Networks via Resource Slicing

Khan, Hamza; Samarakoon, Sumudu; Bennis, Mehdi

doi:10.1109/tvt.2020.2975068

Cited by 39 publications

(24 citation statements)

References 27 publications

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“…where denotes the computing delay between MEC server and the vehicle at time-slot (see (3)). Finally, the total migration cost (x) is defined as follows:…”

Section: =1mentioning

confidence: 99%

A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks

Abouaomar¹,

Mlika²,

Filali³

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MECenabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance.

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“…where denotes the computing delay between MEC server and the vehicle at time-slot (see (3)). Finally, the total migration cost (x) is defined as follows:…”

Section: =1mentioning

confidence: 99%

A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks

Abouaomar¹,

Mlika²,

Filali³

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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“…Instead of using pairwise similarity or pairwise distance, such as correlation clustering, spectral clustering abstracts data points based on the eigenvector from the adjacent matrix, such as the Laplacian matrix from the dataset or constructed graph. The authors in [ 66 , 74 , 75 ] implemented spectral clustering with interference and location-based similarity to group vehicles in an area. Readers that are interested in how spectral clustering is built to separate data points can refer to [ 76 ].…”

Section: Machine Learning For Resource Allocation In Vehicular Networkmentioning

confidence: 99%

Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Nurcahyani

Lee

2021

Sensors

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The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.

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“…Due to the limited wireless resources and time-varying wireless channel conditions, joint optimization of quality adaptation and resource allocation has been largely studied in [15]- [17]. In [15], scheduling and resource allocation algorithm that maps SVC layers to DASH layers and reduces video playback interruptions is presented.…”

Section: Adaptive Quality Selection For Dynamic Streamingmentioning

confidence: 99%

Delay-Sensitive and Power-Efficient Quality Control of Dynamic Video Streaming using Adaptive Super-Resolution

Choi¹,

Yun²,

Kim³

2021

Preprint

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In a decade, the adaptive quality control of video streaming and the super-resolution (SR) technique have been deeply explored. As edge devices improved to have exceptional processing capability than ever before, streaming users can enhance the received image quality to allow the transmitter to compress the images to save its power or pursue network efficiency. In this sense, this paper proposes a novel dynamic video streaming algorithm that adaptively compresses video chunks at the transmitter and separately enhances the quality at the receiver using SR. In order to allow transmission of video chunks with different compression levels and control of the computation burden, we present the adaptive SR network which is optimized by minimizing the weighted sum of losses extracted from different layer outputs. for dynamic video streaming. In addition, we jointly orchestrate video delivery and resource usage, and the proposed video delivery scheme balances the tradeoff well among the average video quality, the queuing delay, buffering time, transmit power, and computation power. Simulation results show that the proposed scheme pursues the quality-of-services (QoS) of the video streaming better than the adaptive quality control without the cooperation of the transmitter and the receiver and the non-adaptive SR network.

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Enhancing Video Streaming in Vehicular Networks via Resource Slicing

Cited by 39 publications

References 27 publications

A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks

A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks

Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Delay-Sensitive and Power-Efficient Quality Control of Dynamic Video Streaming using Adaptive Super-Resolution

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