Power Allocation for Layered Multicast Video Streaming in Non-Orthogonal Multiple Access System

Dani, Muhammad Norfauzi; Al-Abbasi, Ziad Qais; So, Daniel K. C.

doi:10.1109/glocomw.2017.8269084

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…We define the set of users and the total number of users in subgroup s at the n th RB as G respectively. The first (base) layer data stream X (1) consists of the most important data elements for basic quality video content and therefore must be successfully decoded in order to obtain the desired content. The enhancement layer data streams X (s) n (for s ∈ {2, ..., S}) improve the video quality if successfully decoded by the users.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Resource Allocation for Layered Multicast Video Streaming in NOMA Systems

Dani

Tang

et al. 2022

IEEE Trans. Veh. Technol.

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The application of non-orthogonal multiple access (NOMA) to multi-layer multicast video streaming is envisioned to address the explosively growing demand for capacity which is dominated mostly by multimedia content. In this paper, a joint power allocation and subgrouping scheme is developed to enhance the performance of NOMA-based multi-layer multicast systems. An optimization problem is formulated to maximize the overall sum multicast rate whilst satisfying the maximum transmission power and proportional rate constraints. Due to the complexity of the optimization problem, we first derive two power allocation techniques for the 2-layer case considering arbitrary subgrouping which are based on the iterative implementation and closed-form analysis, respectively. We then generalize the lowcomplexity closed-form solution for the general multi-layer case. This scheme successively allocates power to each layer stream while assuring that the minimum target rate and proportionality are guaranteed, particularly for the transmission rate of the highpriority base layer stream. A sub-optimal joint power allocation and subgrouping scheme is also designed by incorporating the power allocation scheme into the proposed iterative subgrouping techniques. Simulation results show the effectiveness of the power allocation and subgrouping schemes in enhancing the sum multicast rate performance. In addition, the proposed power allocation scheme ensures substantially higher rates for the base layer stream, which is crucial in robust delivery of standard quality video to all users.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

Resource Allocation for Layered Multicast Video Streaming in NOMA Systems

Dani

Tang

et al. 2022

IEEE Trans. Veh. Technol.

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“…For example, [7] used Lyapunov optimization to optimally allocate power and bandwidth such that the overall long-term average quality-of-experience (QoE) is maximized for all users. [8] investigated the power required to transmit a multi-layered video for optimal QoE, as measured in terms of video PSNR. [9] investigated the problem of UAV relocation and adjustment of the transmission directionality, when the UAV's transmitted uplink signal interferes with the transmissions in neighboring base stations (BSs).…”

Section: Resource Aware Transmission Of Live Videomentioning

confidence: 99%

An End-to-End Integrated Computation and Communication Architecture for Goal-oriented Networking: A Perspective on Live Surveillance Video

Batabyal¹,

Erçetin²

2022

Preprint

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Real-time video surveillance has become a crucial technology for smart cities, made possible through the large-scale deployment of mobile and fixed video cameras. In this paper, we propose situation-aware streaming, for real-time identification of important events from livefeeds at the source rather than a cloud based analysis. For this, we first identify the frames containing a specific situation and assign them a high scale-of-importance (SI). The identification is made at the source using a tiny neural network (having a small number of hidden layers), which incurs a small computational resource, albeit at the cost of accuracy. The frames with a high SI value are then streamed with a certain required Signal-to-Noise-Ratio (SNR) to retain the frame quality, while the remaining ones are transmitted with a small SNR. The received frames are then analyzed using a deep neural network (with many hidden layers) to extract the situation accurately. We show that the proposed scheme is able to reduce the required power consumption of the transmitter by 38.5% for 2160p (UHD) video, while achieving a classification accuracy of 97.5%, for the given situation.

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“…Recent literature works have proposed NOMA as an energy and spectral efficient air interface for the next generations of wireless networks [7], [17], [19].…”

Section: A Prior Work and Motivationmentioning

confidence: 99%

“…On the other hand, in power domain NOMA, multiplexing is applied through power allocation depending on the channel gain differences among the multiplexed users. As a result, NOMA and CRAN could be tailored into applications where it fulfill the expected demands and challenges of 5G networks [12], [16], [17]. The available resources in the CRAN-NOMA architecture is considered to be the same as in the rest of cellular networks.…”

Section: Introductionmentioning

confidence: 99%

EE Optimization for Downlink NOMA-Based Multi-Tier CRANs

Al-Abbasi

Rabie

2021

IEEE Trans. Veh. Technol.

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Non-orthogonal multiple access (NOMA) is increasingly becoming very attractive in cloud radio access networks (CRANs) to further boost the overall spectral efficiency, connectivity and, capacity of such networks. This paper addresses optimizing the energy efficiency (EE) for the downlink of a NOMAbased two tiers CRAN. The stochastic geometry represented by Poisson Point Process (PPP) distribution is used to decide the number and locations of the base stations (BSs) in each tier within the coverage area. A numerical optimal solution is obtained and compared against a proposed subgradient solution, as well as another proposed unoptimized solution based on the false positioning method. For comparison purposes, two other power allocation techniques are presented to allocate different powers to various BS categories; one allocates the power to each BS based on their relative distances to the cloud-based central station and the other is the bisection based scheme. Two simulation scenarios are presented to examine the performance of the two-tier NOMA-CRANs with NOMA is adopted as the multiple access of each tier in both cases. The first scenario considers heterogeneous CRAN (NOMA-HCRAN) case by using two different BS categories in each tier, namely, the macro-BSs and the RRHs. The second scenario considers a homogeneous CRAN (NOMA-CRAN) case by using the RRHs in both tiers but each tier has different frequency layer to prevent cross tier interference. Simulation results show the promising performance gain can be achieved with the proposed techniques relative to the existing approaches. More specifically, it was illustrated that the proposed subgradient based NOMA CRAN offers better performance than the proposed false positioning based NOMA CRAN, which is in turn better than the existing techniques, in particular, the bisection and the distance based NOMA-CRAN.Index Terms-Non-orthogonal multiple access (NOMA), cloud radio access network (CRAN), false positioning method, resource allocation, energy efficiency.

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Power Allocation for Layered Multicast Video Streaming in Non-Orthogonal Multiple Access System

Cited by 8 publications

References 17 publications

Resource Allocation for Layered Multicast Video Streaming in NOMA Systems

Resource Allocation for Layered Multicast Video Streaming in NOMA Systems

An End-to-End Integrated Computation and Communication Architecture for Goal-oriented Networking: A Perspective on Live Surveillance Video

EE Optimization for Downlink NOMA-Based Multi-Tier CRANs

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