Delivering Resources for Augmented Reality by UAVs: a Reinforcement Learning Approach

Unmanned aerial vehicles (UAVs) are supposed to be used to provide different services from video surveillance to communication facilities during critical and high-demanding scenarios. Augmented reality streaming services are especially demanding in terms of required throughput, computing resources at the user device, as well as user data collection for advanced applications, for example, location-based or interactive ones. This work is focused on the experimental utilization of a framework adopting reinforcement learning (RL) approaches to define the paths crossed by UAVs in delivering resources for augmented reality services. We develop an OpenAI Gym-based simulator that is tuned and tested to study the behavior of UAVs trained with RL to fly around a given area and serve augmented reality users. We provide abstractions for the environment, the UAVs, the users, and their requests. A reward function is then defined to encompass several quality-of-experience parameters. We train our agents and observe how they behave as a function of the number of UAVs and users at different hours of the day.

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Quality of experience for HTTP adaptive streaming services

IEEE Communications Magazine ◽

10.1109/mcom.2012.6178830 ◽

2012 ◽

Vol 50 (4) ◽

pp. 20-27 ◽

Cited By ~ 190

Author(s):

Ozgur Oyman ◽

Sarabjot Singh

Keyword(s):

Quality Of Experience ◽

Adaptive Streaming ◽

Http Adaptive Streaming ◽

Streaming Services

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ASSESSMENT OF THE QUALITY OF EXPERIENCE OF AUGMENTED REALITY APPLICATIONS WITH VIRTUAL ASSISTANTS

Электросвязь ◽

10.34832/elsv.2021.23.10.003 ◽

2021 ◽

Author(s):

М.А. МАКОЛКИНА ◽

А.С. БОРОДИН ◽

Б.О. ПАНЬКОВ

Keyword(s):

Augmented Reality ◽

Quality Of Experience ◽

Augmented Reality Application

Рассмотрено применение виртуальных ассистентов при реализации услуг дополненной реальности (ДР). Разработано приложение ДР с виртуальным ассистентом и проведена оценка качества восприятия с помощью субъективных и объективных методов. The article discusses the use of virtual assistants in the implementation of augmented reality services. An augmented reality application with a virtual assistant was developed and the quality of the experience was assessed using subjective and objective methods.

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Quality of Experience Comparison Between Binocular and Monocular Augmented Reality Display Under Various Occlusion Conditions for Manipulation Tasks with Virtual Instructions

Virtual, Augmented and Mixed Reality. Multimodal Interaction - Lecture Notes in Computer Science ◽

10.1007/978-3-030-21607-8_38 ◽

2019 ◽

pp. 490-499

Author(s):

Ming Qian ◽

John Nicholson ◽

Erin Wang

Keyword(s):

Augmented Reality ◽

Quality Of Experience

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Quality of Experience in Cyber-Physical Social Systems Based on Reinforcement Learning and Game Theory

Future Internet ◽

10.3390/fi10110108 ◽

2018 ◽

Vol 10 (11) ◽

pp. 108 ◽

Cited By ~ 5

Author(s):

Eirini Tsiropoulou ◽

George Kousis ◽

Athina Thanou ◽

Ioanna Lykourentzou ◽

Symeon Papavassiliou

Keyword(s):

Game Theory ◽

Reinforcement Learning ◽

Time Management ◽

Quality Of Experience ◽

Social Systems ◽

Cost Effective ◽

Superior Performance ◽

Operational Effectiveness ◽

Effective Manner

This paper addresses the problem of museum visitors’ Quality of Experience (QoE) optimization by viewing and treating the museum environment as a cyber-physical social system. To achieve this goal, we harness visitors’ internal ability to intelligently sense their environment and make choices that improve their QoE in terms of which the museum touring option is the best for them and how much time to spend on their visit. We model the museum setting as a distributed non-cooperative game where visitors selfishly maximize their own QoE. In this setting, we formulate the problem of Recommendation Selection and Visiting Time Management (RSVTM) and propose a two-stage distributed algorithm based on game theory and reinforcement learning, which learns from visitor behavior to make on-the-fly recommendation selections that maximize visitor QoE. The proposed framework enables autonomic visitor-centric management in a personalized manner and enables visitors themselves to decide on the best visiting strategies. Experimental results evaluating the performance of the proposed RSVTM algorithm under realistic simulation conditions indicate the high operational effectiveness and superior performance when compared to other recommendation approaches. Our results constitute a practical alternative for museums and exhibition spaces meant to enhance visitor QoE in a flexible, efficient, and cost-effective manner.

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Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video

Applied Sciences ◽

10.3390/app10051793 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1793

Author(s):

Lina Du ◽

Li Zhuo ◽

Jiafeng Li ◽

Jing Zhang ◽

Xiaoguang Li ◽

...

Keyword(s):

Video Compression ◽

Quality Of Experience ◽

Feature Vector ◽

Video Quality ◽

Superior Performance ◽

Adaptive Streaming ◽

Input Feature ◽

Streaming Services ◽

Content Characteristics

DASH (Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol)) as a universal unified multimedia streaming standard selects the appropriate video bitrate to improve the user’s Quality of Experience (QoE) according to network conditions, client status, etc. Considering that the quantitative expression of the user’s QoE is also a difficult point in itself, this paper researched the distortion caused due to video compression, network transmission and other aspects, and then proposes a video QoE metric for dynamic adaptive streaming services. Three-Dimensional Convolutional Neural Networks (3D CNN) and Long Short-Term Memory (LSTM) are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. While accounting for the fluctuation in the quality of a video caused by bitrate switching on the QoE, other factors such as video content characteristics, video quality and video fluency, are combined to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to dynamically describe the relationship between the input feature vector and the value of the Mean Opinion Score (MOS). The experimental results on different datasets demonstrate that the prediction accuracy of the proposed method can achieve superior performance over the state-of-the-art methods, which proves the proposed QoE model can effectively guide the client’s bitrate selection in dynamic adaptive streaming media services.

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Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification

Applied Sciences ◽

10.3390/app9112297 ◽

2019 ◽

Vol 9 (11) ◽

pp. 2297

Author(s):

Kyeongseon Kim ◽

Dohyun Kwon ◽

Joongheon Kim ◽

Aziz Mohaisen

Keyword(s):

Video Streaming ◽

Waiting Time ◽

Quality Of Experience ◽

Video Quality ◽

Live Video ◽

Streaming Services ◽

Online Streaming ◽

Over The Top ◽

User Content

As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms.

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