Wireless control using reinforcement learning for practical web QoE

Moura, Henrique; Macedo, Daniel F.; Vieira, Marcos A. M.

doi:10.1016/j.comcom.2020.02.032

Cited by 9 publications

(16 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, QoE prediction by ML-based methods is indispensable to be a crucial part for optimizing QoE in IoT systems. When QoE can be approximated in real-time, it can be used as optimization objectives to solve variety of problems e.g., QoE-aware adaptive rate control in streaming services [5], QoE-aware power controls on MEC/Fog/Cloud [6], QoE-aware network controls on SDN/NFV enabled networks [7]. For this reason, prior works on QoE-aware optimization and control are required to further investigation from many aspects of problems.…”

Section: Motivationmentioning

confidence: 99%

“…User and Context related IFs can be gender, age, social, characteristics, education, user location, background, usage history, living environments, job, personal interest and others. These factors can be measured by using User Engagement metrics such as records of user profile and background, number of downloads, average visit time, screen views per visits, retention rate, user event tracking (e.g., search history), and others [49], [50], [51], [52], [53], [54], [6], [55], [51]. All of these measurements can be utilized by Machine Learning approaches to tailor quality of experience at User level, which is dependent on marketing strategies.…”

Section: A Qoe Cause Factorsmentioning

confidence: 99%

“…At the Sensing level, channel quality is the main factor affecting user experiences. In short, QoS metrics, typically used in most systems, are bandwidth, signal strength, bit error rate, dropping probability, power consumption [6], [54]. Even though IoT devices can be physically operated by these technologies : WiFi, NB-IoT, LoRA, LTE, 5G, all of them have similar control parameters such as bandwidth control, sub-carriers, modulation scheme, coding, transmission power.…”

Section: A Qoe Cause Factorsmentioning

confidence: 99%

“…As a result, many prior works proposed QoE-related things by using objective QoE metrics. QoE metrics can be estimated from various factors : network delay and quality, [50], [61], [62], [63], [77], [78], [64], [54], [6], latency [79], energy consumption [80], [81] processing and completion time [82], resource states [83], [81], contentrelated metrics (e.g., voice, video, image) [84], [85], [86], [87], and user-related metrics [49], [81] User and Context related IFs : gender, age, social, characteristics, education, user location (e.g., home, workplaces), live style, background, usage history, living environment, workplace, job, personal interest, memory experiences user engagement metrics : user background and profiles, number of downloads, average visit time, screen views per visits, retention rate, number of active users, user event tracking (e.g., search history), user satisfaction metrics (e.g., app rating, touch heatmap, in-app feedback), user perception metrics (app crashes, speed, latency) [49], [50], [51], [52], [53], [54], [6], [55], [51] marketing strategies and tuning designs for users e.g., machine learning approaches for tailor-made experience for each user Application (Contentrelated)…”

Section: B Qoe Measurement and Indicatormentioning

confidence: 99%

See 3 more Smart Citations

QoE-Driven IoT Architecture: A Comprehensive Review on System and Resource Management

et al. 2022

View full text Add to dashboard Cite

Internet of Things (IoT) services have grown substantially in recent years. Consequently, IoT service providers (SPs) are emerging in the market and competing to offer their services. Many IoT applications utilize these services in an integrated manner with different Quality-of-Service (QoS) requirements. Thus, the provisioning of end-to-end QoS is getting more indispensable for IoT platforms. However, provisioning the system by using only QoS metrics without considering user experiences is not sufficient. Recently, Quality of Experience (QoE) model has become a promising approach to quantify actual user experiences of services. A holistic design approach that considers constraints of various QoS/QoE metrics together is needed to satisfy requirements of these applications and services. Besides, IoT services may operate in environments with limited resources. Therefore, effective management of services and system resources is essential for QoS/QoE support. This paper provides a comprehensive survey for the state-of-the-art studies on IoT services with QoS/QoE perspective. Our contributions are threefold: (1) QoEdriven architecture is demonstrated by classifying vital components according to QoE-related functions in prior studies, (2) QoE metrics and QoE optimization objectives are classified by corresponding system and resource control problems in the architecture, and (3) QoE-aware resource management e.g., QoE-aware offloading, placement and data caching policies with recent Machine Learning approaches are extensively reviewed. INDEX TERMSInternet of Things, Quality of Service, Quality of Experience, IoT services, IoT applications, QoS for IoT services, QoS metrics, QoE metrics, IoT architecture Recently, emerging IoT architectures with multi-layers, e.g., Mobile Edge Computing (MEC), Fog Computing and Cloud Computing, have been proposed to improve user experiences.

show abstract

Section: Motivationmentioning

confidence: 99%

Section: A Qoe Cause Factorsmentioning

confidence: 99%

Section: A Qoe Cause Factorsmentioning

confidence: 99%

Section: B Qoe Measurement and Indicatormentioning

confidence: 99%

See 2 more Smart Citations

QoE-Driven IoT Architecture: A Comprehensive Review on System and Resource Management

et al. 2022

View full text Add to dashboard Cite

show abstract

“…• Tuning Wi-Fi parameters: Moura et al [24] use the estimated MOS of user flows to reconfigure the channel and the transmit power of Wi-Fi access points;…”

Section: Proposed Solutionmentioning

confidence: 99%

Estimating Video on Demand QoE From Network QoS Through ICMP Probes

Miranda

Macedo

Marquez-Barja

2022

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

With the increasing traffic of Video on Demand (VoD), network providers are seeking to deliver high Quality of Experience (QoE) for their users. Many methods have been proposed to assess VoD-related QoE. Some of them rely on client instrumentation and reporting QoE information to network elements, such as Server and Network Assisted DASH, others are based on statistical methods that make QoE inferences using monitored network conditions, such as throughput and delays. In this article, we present a practical method to estimate QoE for VoD using the widely supported Internet Control Message Protocol (ICMP) probes. Measured network conditions are used as input to a Machine Learning (ML) model that estimates QoE in terms of Mean Opinion Score (MOS), based on the ITU-T P.1203 Recommendation. The estimation encompasses video quality switches and playback stalls. We estimate MOS with an average Root Mean Square Error (RMSE) of 1.05 for a catalog of 25 different videos, training a model with sessions of the shortest video, and evaluating the generalization to the full catalog. We performed experiments using a virtualized setup as well as in a Wide Area Network.

show abstract