A stochastic approximation resource allocation approach for HD live streaming

Mostafavi, Seyedakbar; Dehghan, Mehdi

doi:10.1007/s11235-016-0161-7

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…Liang et al. ( 2011 ) investigated optimal bandwidth sharing in multiswarm multiparty P2P video-conferencing systems. Chen et al ( 2012 ) considered resource allocation for P2P multiparty conferencing applications where quality of service (QoS) guarantee is a crucial challenge, and formulated the resource allocation problem as utility maximization.…”

Section: Related Workmentioning

confidence: 99%

“…Mostafavi and Dehghan (2016) considered bandwidth sharing and allocation in helper-assisted P2P streaming using non-cooperative game theory and double auction, and proposed an algorithm to reach significant performance improvements in terms of utility per selling helpers. They Mostafavi and Dehghan (2017) also considered resource allocation for HD live streaming and proposed a decentralized, stochastic approximation helper selection mechanism which is adaptable to supply and demand pattern of various video channels. Recently, Li et al (2017) also developed a utility maximization model for resource allocation of P2P inelastic services.…”

Section: Related Workmentioning

confidence: 99%

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Optimal resource allocation for multiclass services in peer-to-peer networks via successive approximation

Sun

Huan

2021

Oper Res Int J

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Peer-to-peer (P2P) networks support a wide variety of network services including elastic services such as file-sharing and downloading and inelastic services such as real-time multiparty conferencing. Each peer who acquires a service will receive a certain level of satisfaction if the service is provided with a certain amount of resource. The utility function is used to describe the satisfaction of a peer when acquiring a service. In this paper we consider optimal resource allocation for elastic and inelastic services and formulate a utility maximization model which is an intractable and difficult non-convex optimization problem. In order to resolve it, we apply the successive approximation method and approximate the non-convex problem to a serial of equivalent convex optimization problems. Then we develop a gradientbased resource allocation scheme to achieve the optimal solutions of the approximations. After a serial of approximations, the proposed scheme can finally converge to an optimal solution of the primal utility maximization model for resource allocation which satisfies the Karush-Kuhn-Tucker conditions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimal resource allocation for multiclass services in peer-to-peer networks via successive approximation

Sun

Huan

2021

Oper Res Int J

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“…These protocols are based on the Global Positioning System (GPS) like the Greedy Perimeter Stateless Routing (GPSR) protocol, the most commonly known in this category [24][25].…”

Section: Geographical Routing Protocolsmentioning

confidence: 99%

A QoS-Assured and Mobility-Aware Routing Protocol for MANETs

Mostafavi

Hakami

Paydar

2020

JOIV : Int. J. Inform. Visualization

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In Mobile Ad Hoc Networks (MANETs), lack of a fixed infrastructure, dynamic network topology, device mobility and data communication over wireless channels make the multi-hop routing a very challenging task. Due to mission-critical applications of MANET, dealing with these challenges through the design of a Quality of Service (QoS)-assured protocol is a substantial problem. Mobility in MANETs is commonly considered as a negative factor on quality, although we suggest that the right approach to mobility awareness using wisely selected metrics can lead to a robust and QoS-assured protocol. In this paper, we propose QMAR-AODV, a QoS-assured Mobility-Aware Routing protocol which is an optimized version of AODV protocol. We utilize a combination of stability and quality metrics including Mobility Ratio (MR(C,E)) between nodes in a route, Energy Efficiency and congestion load to choose the most stable and QoS-assured routes. Our simulation results show that QMAR-AODV protocol outperforms E2E-LREEMR and reduces route instability, end-to-end delay, data retransmissions and packet loss by 8.3% 10.9% 10.6% and 5.4 respectively, while increases data reception and network throughput by 5.1% and 4.8% respectively, compared to E2E-LREEMR routing protocol.

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“…There are also some similar works in a resource allocation context, like the work of Mostafavi and Dehghan, where the objective function has an uncertain value because of some random variation of parameters, and they used an iterative procedure to achieve convergence and they are categorized in the stochastic approximation framework.…”

Section: Introductionmentioning

confidence: 99%

Learning‐based resource allocation in D2D communications with QoS and fairness considerations

Rashed

Shahbazian

Ghorashi

2017

Trans Emerging Tel Tech

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In device-to-device (D2D) communications, D2D users establish a direct link by utilizing the cellular users' spectrum to increase the network spectral efficiency. However, due to the higher priority of cellular users, interference imposed by D2D users to cellular ones should be controlled by channel and power allocation algorithms. Due to the unknown distribution of dynamic channel parameters, learning-based resource allocation algorithms work more efficient than classic optimization methods. In this paper, the problem of the joint channel and power allocation for D2D users in realistic scenarios is formulated as an interactive learning problem, where the channel state information of selected channels is unknown to the decision center and learned during the allocation process. In order to achieve the maximum reward function by choosing an action (channel and power level) for each D2D pair, a recency-based Q-learning method is introduced to find the best channel-power for each D2D pair. The proposed method is shown to achieve logarithmic regret function asymptotically, which makes it an order optimal policy, and it converges to the stable equilibrium solution. The simulation results confirm that the proposed method achieves better responses in terms of network sum rate and fairness criterion in comparison with conventional learning methods and random allocation.

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A stochastic approximation resource allocation approach for HD live streaming

Cited by 10 publications

References 36 publications

Optimal resource allocation for multiclass services in peer-to-peer networks via successive approximation

Optimal resource allocation for multiclass services in peer-to-peer networks via successive approximation

A QoS-Assured and Mobility-Aware Routing Protocol for MANETs

Learning‐based resource allocation in D2D communications with QoS and fairness considerations

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