Broadcast the User Chosen Content over LTE in a Simplified Approach Using Evolved Multimedia Broadcast Multicast Service (eMBMS) with Optimized MCE Scheduling Algorithm

Damera, Baburao; Babu, P. Chitti

doi:10.1109/icica.2014.28

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…Multicast transmission played a broad role in different areas. Damera et al [26] constructed a new feasible architectural model to transmit the required content to the user using multicell transmission. The Signal Noise Ratio was improved using the The hyperparameter of the entropy term γ…”

Section: Tablesmentioning

confidence: 99%

COCAM: A Cooperative Video Edge Caching and Multicasting Approach Based on Multi-Agent Deep Reinforcement Learning

Shi

Fan

et al. 2023

Preprint

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The evolution of the Internet of Things technology has boosted the drastic increase in network traffic demand. Caching and multicasting are effective approaches to alleviate the backhaul burden of networks and reduce service latency. However, existing works do not jointly exploit the advantages of these two approaches. In this paper, we propose COCAM, a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning to minimize the transmission number in the multi-clouds scenario with limited storage capacity in each mobile edge server. Specifically, by integrating a cooperative transmission model with the caching model, we provide a concrete formulation of the joint problem. Then, We cast this decision-making problem as a multi-agent extension of the Markov decision process and propose a multi-agent actor-critic algorithm in which each agent learns a local caching policy and observes the information of neighboring agents as part of the environment. Finally, to validate the COCAM algorithm, we conduct extensive experiments on a real-world dataset. The results show that our proposed algorithm outperforms other baseline algorithms in terms of the number of video transmissions.

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Section: Tablesmentioning

confidence: 99%

COCAM: A Cooperative Video Edge Caching and Multicasting Approach Based on Multi-Agent Deep Reinforcement Learning

Shi

Fan

et al. 2023

Preprint

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show abstract

“…Multicast transmission is extensively utilized in edge networks, demonstrating its efficacy in enhancing network performance by reducing bandwidth, routing, and cost [31]. Damera et al [32] constructed a new feasible architectural model to transmit the required content to the user using the multicell transmission. The Signal Noise Ratio was improved using the multicell transmission.…”

Section: Multicasting Algorithmsmentioning

confidence: 99%

COCAM: a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning in multi-clouds environment

Shi,

Fan,

et al. 2023

J Cloud Comp

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The evolution of the Internet of Things technology (IoT) has boosted the drastic increase in network traffic demand. Caching and multicasting in the multi-clouds scenario are effective approaches to alleviate the backhaul burden of networks and reduce service latency. However, existing works do not jointly exploit the advantages of these two approaches. In this paper, we propose COCAM, a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning to minimize the transmission number in the multi-clouds scenario with limited storage capacity in each edge cloud. Specifically, by integrating a cooperative transmission model with the caching model, we provide a concrete formulation of the joint problem. Then, we cast this decision-making problem as a multi-agent extension of the Markov decision process and propose a multi-agent actor-critic algorithm in which each agent learns a local caching strategy and further encompasses the observations of neighboring agents as constituents of the overall state. Finally, to validate the COCAM algorithm, we conduct extensive experiments on a real-world dataset. The results show that our proposed algorithm outperforms other baseline algorithms in terms of the number of video transmissions. Graphical Abstract

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“…In [33], Damera and Babu proposed an MBS based architecture that offers multiple content to viewers at the same time. The proposed scheme aims to increase QoS of the user, business revenues for the service provider, and provide efficient network resources utilization.…”

Section: Background and Related Workmentioning

confidence: 99%

Multicast at Edge: An Edge Network Architecture for Service-Less Crowdsourced Live Video Multicast

et al. 2021

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Using smartphones, tablets, and other portable/handheld devices, we have become more reliant on the video streaming services for entertainment and remote work. Mobile data traffic has grown eighteen folds over the past five years accounting for the majority of IP traffic. YouTube Live, Facebook Live, Twitch, DouYu and other streaming as well as video conferencing services have increased in popularity so at any given moment they serve thousands of live video streams to millions of users. The Enhanced Multimedia Broadcast Multimedia Service (eMBMS) is the standard multicast protocol for 5G networks. Cellular multicast has gained considerable attention to efficiently utilize the limited spectrum to transmit multimedia content to cellular sites with co-located viewers, lowering the cost, and maximizing the Quality of Experience (QoE). However, popular live video content providers use unicast mode for live video delivery and have limited support in the eMBMS service-oriented network architecture. In this paper, we propose an overlay network architecture to augment eMBMS to address the limitations of the standard eMBMS architecture and enable service-less multicast for crowdsourced live video providers. We propose a Virtual Network Function (VNF) service that identifies potential multicast scenarios based on user requests for a live video within a confined area. The VNF Application Server collects information, validates a potential multicast scenario, and initiates an ad-hoc multicast service on the fly. We use a real-world dataset of Facebook Live videos to evaluate the proposed architecture. The simulation results depict considerable advantages in terms of cost, efficiency, and Quality of Experience (QoE). Our results show that the proposed architecture provides significant benefits in bandwidth saving at the backhaul, transit, and RAN links.

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Broadcast the User Chosen Content over LTE in a Simplified Approach Using Evolved Multimedia Broadcast Multicast Service (eMBMS) with Optimized MCE Scheduling Algorithm

Cited by 9 publications

References 4 publications

COCAM: A Cooperative Video Edge Caching and Multicasting Approach Based on Multi-Agent Deep Reinforcement Learning

COCAM: A Cooperative Video Edge Caching and Multicasting Approach Based on Multi-Agent Deep Reinforcement Learning

COCAM: a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning in multi-clouds environment

Multicast at Edge: An Edge Network Architecture for Service-Less Crowdsourced Live Video Multicast

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