Joint offloading and streaming in mobile edges: a deep reinforcement learning approach
2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), 2019•ieeexplore.ieee.org
This paper proposes a joint dynamic video streaming and deep reinforcement learning
(DRL) based offloading method in mobile edge computing systems. In order to utilize video
services in mobile edge networks, efficient streaming and offloading algorithms are
essentially required. In this paper, therefore, a novel dynamic offloading algorithm is
proposed and the algorithm is fundamentally based on deep Q-network (DQN) which is one
of widely used deep reinforcement learning algorithms.
(DRL) based offloading method in mobile edge computing systems. In order to utilize video
services in mobile edge networks, efficient streaming and offloading algorithms are
essentially required. In this paper, therefore, a novel dynamic offloading algorithm is
proposed and the algorithm is fundamentally based on deep Q-network (DQN) which is one
of widely used deep reinforcement learning algorithms.
This paper proposes a joint dynamic video streaming and deep reinforcement learning (DRL) based offloading method in mobile edge computing systems. In order to utilize video services in mobile edge networks, efficient streaming and offloading algorithms are essentially required. In this paper, therefore, a novel dynamic offloading algorithm is proposed and the algorithm is fundamentally based on deep Q-network (DQN) which is one of widely used deep reinforcement learning algorithms.
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