Seamless Dynamic Adaptive Streaming in LTE/Wi-Fi Integrated Network under Smartphone Resource Constraints

Koo, Jonghoe; Yi, Juheon; Kim, Joongheon; Hoque, Mohammad A.; Choi, Sunghyun

doi:10.1109/tmc.2018.2863234

Cited by 47 publications

(15 citation statements)

References 39 publications

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“…Then, the Lyapunov function is given as 𝕃(𝑄[𝑡]) = 1 2 (𝑄[𝑡]) 2 . According to [16,17], the upper bound on the conditional Lyapunov drift is as follows:…”

Section: Lyapunov-based Dynamic Resource Allocation Algorithm For Rea...mentioning

confidence: 99%

Dynamic resource scheduling for real-time group broadcasting in 6G cellular vehicular networks

Jung¹,

Levorato²,

Kim³

2022

ITU J-FET

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Vehicle-to-Vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems.The 3rd Generation Partnership Project (3GPP) is designed to support V2V communication without any infrastructure using sensing-based Semi-Persistent Scheduling (SPS) in order to avoid resource collisions for Cooperative Awareness Messages (CAMs) transmission. However, the legacy system suffers from significant collisions in a traffic congestion environment and resource waste in light traffic conditions. To address these problems, in this paper we propose a novel dynamic resource scheduling algorithm for real-time group broadcasting, inspired by the Lyapunov optimization framework, where the optimization objective is to minimize time-average failure probability subject to queue stability. Through extensive simulations, we show that the proposed scheduling algorithm outperforms the standard in both traffic jams and leisurely road environments.

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“…Then, the Lyapunov function is given as 𝕃(𝑄[𝑡]) = 1 2 (𝑄[𝑡]) 2 . According to [16,17], the upper bound on the conditional Lyapunov drift is as follows:…”

Section: Lyapunov-based Dynamic Resource Allocation Algorithm For Rea...mentioning

confidence: 99%

Dynamic resource scheduling for real-time group broadcasting in 6G cellular vehicular networks

Jung¹,

Levorato²,

Kim³

2022

ITU J-FET

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“…This paper designs a dynamic power allocation method for energy-efficient operations while guaranteeing queue stability. Note that this kind of delay-aware timeaverage optimization is definitely needed for computing system design and implementation [38]- [41]. Our considering network architecture is as shown in Fig.…”

Section: ) Two-stage Mobile Charging Matching/schedulingmentioning

confidence: 99%

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

et al. 2021

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In modern networks, the use of drones as mobile base stations (MBSs) has been discussed for coverage flexibility. However, the realization of drone-based networks raises several issues. One of critical issues is drones are extremely power-hungry. To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones. Motivated by the fact that the charging drones also need to be charged, we deploy ground-mounted charging towers for delivering energy to the charging drones. We introduce a new energy-efficiency maximization problem, which is partitioned into two independently separable tasks. More specifically, as our first optimization task, two-stage charging matching is proposed due to the inherent nature of our network model, where the first matching aims to schedule between charging towers and charging drones while the second matching solves the scheduling between charging drones and MBS drones. We analyze how to convert the formulation containing non-convex terms to another one only with convex terms. As our second optimization task, each MBS drone conducts energy-aware time-average transmit power allocation minimization subject to stability via Lyapunov optimization. Our solutions enable the MBS drones to extend their lifetimes; in turn, network coverage-time can be extended.INDEX TERMS Cellular network, charging drone, coverage-time, mobile base station, scheduling.

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“…Kim et al [3,5] design a dynamic control algorithm for time-average streaming quality (i.e., peak-signal-to-noise ratio (PSNR)) maximization subject to transmit buffer stability in wireless video networks. Koo et al [6,7] also propose a novel dynamic adaptive streaming over HTTP (DASH)-based mechanism for video streaming quality maximization under the consideration of battery status, LTE data quota, and stability in hybrid LTE and WiFi networks.…”

Section: Adaptive Video Streamingmentioning

confidence: 99%

Dynamic Decision-Making for Stabilized Deep Learning Software Platforms

Park¹,

Kim²,

Kim³

2020

Advances and Applications in Deep Learning

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This chapter introduces a dynamic and low-complexity decision-making algorithm which aims at time-average utility maximization in real-time deep learning platforms, inspired by Lyapunov optimization. In deep learning computation, large delays can happen due to the fact that it is computationally expensive. Thus, handling the delays is an important issue for the commercialization of deep learning algorithms. In this chapter, the proposed algorithm observes system delays at first formulated by queue-backlog, and then it dynamically conducts sequential decision-making under the tradeoff between utility (i.e., deep learning performance) and system delays. In order to evaluate the proposed decision-making algorithm, the performance evaluation results with real-world data are presented under the applications of super-resolution frameworks. Lastly, this chapter summarizes that the Lyapunov optimization algorithm can be used in various emerging applications.

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Seamless Dynamic Adaptive Streaming in LTE/Wi-Fi Integrated Network under Smartphone Resource Constraints

Cited by 47 publications

References 39 publications

Dynamic resource scheduling for real-time group broadcasting in 6G cellular vehicular networks

Dynamic resource scheduling for real-time group broadcasting in 6G cellular vehicular networks

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

Dynamic Decision-Making for Stabilized Deep Learning Software Platforms

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