Dynamic Resource Allocation and Layer Selection for Scalable Video Streaming in Femtocell Networks: A Twin-Time-Scale Approach

Yang, Jian; Si, Peng; Wang, Zilei; Jiang, Xiaofeng; Hanzo, Lajos

doi:10.1109/tcomm.2017.2783974

Cited by 15 publications

(8 citation statements)

References 44 publications

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“…Such a safe layer can also be inserted into other RL problems for satisfying the constraints, as long as the constraints can be equivalently transformed into the constraints on the action of each time step with known expressions, as exemplified by (21). 4…”

Section: A Safe Layer For Actor Networkmentioning

confidence: 99%

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Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming

Liu

Zhao

Yang

et al. 2020

Preprint

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Predictive power allocation is conceived for power-efficient video streaming over mobile networks using deep reinforcement learning. The goal is to minimize the accumulated energy consumption over a complete video streaming session for a mobile user under the quality of service constraint that avoids video playback interruptions. To handle the continuous state and action spaces, we resort to deep deterministic policy gradient (DDPG) algorithm for solving the formulated problem. In contrast to previous predictive resource policies that first predict future information with historical data and then optimize the policy based on the predicted information, the proposed policy operates in an on-line and end-to-end manner. By judiciously designing the action and state that only depend on slowly-varying average channel gains, the signaling overhead between the edge server and the base stations can be reduced, and the dynamics of the system can be learned effortlessly. To improve the robustness of streaming and accelerate learning, we further exploit the partially known dynamics of the system by integrating the concepts of safer layer, post-decision state, and virtual experience into the basic DDPG algorithm. Our simulation results show that the proposed polices converge to the optimal policy derived based on perfect prediction of the future large-scale channel gains and outperforms the first-predictthen-optimize policy in the presence of prediction errors. By harnessing the partially known model of the system dynamics, the convergence speed can be dramatically improved.

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Section: A Safe Layer For Actor Networkmentioning

confidence: 99%

“…over cellular networks enables mobile users to watch the requested video while downloading [3][4][5]. To avoid video stalling for a user experiencing hostile channel conditions, a base station (BS) can increase its transmit power for ensuring that the video segment is downloaded before being played.…”

mentioning

confidence: 99%

Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming

Liu

Zhao

Yang

et al. 2020

Preprint

Self Cite

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“…Further, control of the amount of receiving chunks depending on stochastic network states has been largely neglected in above existing researches about video delivery. Even though the authors of [18] and [26] maximize the long-term time-average video quality under the various constraints, their metrics representing video quality is obtained by averaging the number of quality selections at each time slot. This method would be not enough to evaluate the user's quality of service, especially when the transmission rate varies over the video streaming service time.…”

Section: Introductionmentioning

confidence: 99%

Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks

Choi

et al. 2019

IEEE Trans. Wireless Commun.

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This paper proposes a video delivery strategy for dynamic streaming services which maximizes time-average streaming quality under a playback delay constraint in wireless caching networks. The network where popular videos encoded by scalable video coding are already stored in randomly distributed caching nodes is considered under adaptive video streaming concepts, and distance-based interference management is investigated in this paper. In this network model, a streaming user makes delay-constrained decisions depending on stochastic network states: 1) caching node for video delivery, 2) video quality, and 3) the quantity of video chunks to receive. Since wireless link activation for video delivery may introduce delays, different timescales for updating caching node association, video quality adaptation, and chunk amounts are considered. After associating with a caching node for video delivery, the streaming user chooses combinations of quality and chunk amounts in the small timescale.The dynamic decision making process for video quality and chunk amounts at each slot is modeled using Markov decision process, and the caching node decision is made based on the framework of Lyapunov optimization. Our intensive simulations verify that the proposed video delivery algorithm works reliably and also can control the tradeoff between video quality and playback latency. Index TermsWireless caching network, video delivery, stochastic network optimization, Lyapunov optimization, Markov decision process M. Choi and J. Kim are with the

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“…Joint downlink scheduling and delivery in HetNets were addressed in [20], where the interference effect on a BS from other BSs assumed to transmit with peak power was considered. In [21], interference effects between a macro BS and femto BSs were captured by using a monetary cost model. However, in [20], [21] it was still assumed that no interference exists among users in the same femtocell.…”

mentioning

confidence: 99%

“…In [21], interference effects between a macro BS and femto BSs were captured by using a monetary cost model. However, in [20], [21] it was still assumed that no interference exists among users in the same femtocell. Meanwhile, BS association in caching networks was proposed in [22], [23] and the interference caused by multiple BSs was considered without any strict constraint for interference models; however, adaptive power allocations for interference management were not considered in these studies.…”

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confidence: 99%

Joint Distributed Link Scheduling and Power Allocation for Content Delivery in Wireless Caching Networks

Choi

Molisch

Kim³

2019

Preprint

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In wireless caching networks, the design of the content delivery method must consider random user requests, caching states, network topology, and interference management. In this paper, we establish a general framework for content delivery in wireless caching networks without stringent assumptions that restrict the network structure, delivery link, and interference model. Based on the framework, we propose a dynamic and distributed link scheduling and power allocation scheme for content delivery that is assisted by belief-propagation (BP) algorithms. Considering content-requesting users and potential caching nodes, the scheme achieves three critical purposes of wireless caching networks: 1) limiting the delay of user request satisfactions, 2) maintaining the power efficiency of caching nodes, and 3) managing interference among users. In addition, we address the intrinsic problem of the BP algorithm in our network model, proposing a matching algorithm for one-to-one link scheduling. Simulation results show that the proposed scheme provides almost the same delay performance as the optimal scheme found through an exhaustive search at the expense of a little additional power consumption and does not require a clustering method and orthogonal resources in a large-scale D2D network.

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Dynamic Resource Allocation and Layer Selection for Scalable Video Streaming in Femtocell Networks: A Twin-Time-Scale Approach

Cited by 15 publications

References 44 publications

Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming

Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming

Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks

Joint Distributed Link Scheduling and Power Allocation for Content Delivery in Wireless Caching Networks

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