NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming
Video streaming services strive to support high-quality videos at higher resolutions and frame rates to improve the quality of experience (QoE). However, high-quality videos consume considerable amounts of energy on mobile devices. This paper proposes NeuSaver, which reduces the power consumption of mobile devices when streaming videos by applying an adaptive frame rate to each video chunk without compromising user experience. NeuSaver generates an optimal policy that determines the appropriate frame rate for each video chunk using reinforcement learning (RL). The RL model automatically learns the policy that maximizes the QoE goals based on previous observations. NeuSaver also uses an asynchronous advantage actor-critic algorithm to reinforce the RL model quickly and robustly. Streaming servers that support NeuSaver preprocesses videos into segments with various frame rates, which is similar to the process of creating videos with multiple bit rates in dynamic adaptive streaming over HTTP. NeuSaver utilizes the commonly used H.264 video codec. We evaluated NeuSaver in various experiments and a user study through four video categories along with the state-of-the-art model. Our experiments showed that NeuSaver effectively reduces the power consumption of mobile devices when streaming video by an average of 16.14% and up to 23.12% while achieving high QoE.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement Learning (RL)Similar Papers 제목 키워드 기반
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks
Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mobile robots have shown huge potential for improving human productivity. These mobile agents require low power/energy consumption to have a long lifes…
Model SelectionHow to minimize the energy consumption in mobile ad-hoc networks
In this work we are interested in the problem of energy management in Mobile Ad-hoc Network (MANET). The solving and optimization of MANET allow assisting the users to efficiently use their devices in order to minimize t…
energy managementManagementPower Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning
In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a re…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach
Mobile edge computing (MobEC) builds an Information Technology (IT) service environment to enable cloud-computing capabilities at the edge of mobile networks. To tackle the restrictions in the battery power and computati…
Cloud ComputingEdge-computingQ-Learningreinforcement-learning+1Adaptive Pruning of Neural Language Models for Mobile Devices
Neural language models (NLMs) exist in an accuracy-efficiency tradeoff space where better perplexity typically comes at the cost of greater computation complexity. In a software keyboard application on mobile devices, th…