QoE-Driven Transmission-Aware Cache Placement and Cooperative Beamforming Design in Cloud-RANs

Sun, Ruijin; Wang, Ying; Cheng, Nan; Lyu, Ling; Zhang, Shan; Zhou, Haibo; Shen, Xuemin

doi:10.1109/tvt.2019.2952726

Cited by 37 publications

(28 citation statements)

References 34 publications

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“…Zhikai et al [20] proposed an active video content caching (RCC) scheme to minimize transmission delay. Sun et al [21] designed cache placement and short-term transfer strategies for long-term-aware transmission to improve the QOE.…”

Section: Traditional Video Application Methods Over Cvismentioning

confidence: 99%

Deep Learning for Video Application in Cooperative Vehicle-Infrastructure System: A Comprehensive Survey

Dai

2022

Applied Sciences

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Video application is a research hotspot in cooperative vehicle-infrastructure systems (CVIS) which is greatly related to traffic safety and the quality of user experience. Dealing with large datasets of feedback from complex environments is a challenge when using traditional video application approaches. However, the in-depth structure of deep learning has the ability to deal with high-dimensional data sets, which shows better performance in video application problems. Therefore, the research value and significance of video applications over CVIS can be better reflected through deep learning. Firstly, the research status of traditional video application methods and deep learning methods over CVIS were introduced; the existing video application methods based on deep learning were classified according to generative and discriminative deep architecture. Then, we summarized the main methods of deep learning and deep reinforcement learning algorithms for video applications over CVIS, and made a comparative study of their performances. Finally, the challenges and development trends of deep learning in the field were explored and discussed.

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Section: Traditional Video Application Methods Over Cvismentioning

confidence: 99%

Deep Learning for Video Application in Cooperative Vehicle-Infrastructure System: A Comprehensive Survey

Dai

2022

Applied Sciences

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“…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%

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

et al. 2022

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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.

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“…A probabilistic user request model is used 16,19 . First, sort all

F

files in the media server in a descending order based on their request probabilities.…”

Section: System Modelmentioning

confidence: 99%

“…Therefore, the cache placement and the transmission strategies are coupled with each other. Some research works have been done for ground cellular networks to jointly consider the cache placement and content delivery, 16,17 but the cache updating strategy for dynamic topology is not involved. In this paper, we consider a multigroup multicast transmission scenario for the cache-enabled UD-LSC network, and both the dynamic cache updating problem and cooperative multicast transmission problem are studied for UD-LSC networks to minimize the ground user's average CDD.…”

mentioning

confidence: 99%

Cooperative multigroup multicast beamforming for cache‐enabled ultra‐dense low earth orbit satellite constellation networks

Tong

Wang

Zhao

et al. 2022

Satell Commun Network

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The ultra-dense low earth orbit (LEO) satellite constellation (UD-LSC) networks are expected to be incorporated in future wireless networks, particularly 6G networks, to provide global wireless access with enhanced data rates. This paper investigates the downlink multigroup multicast transmission for UD-LSC networks, in which LEO satellites provide the multicast service for ground users with full frequency reuse. Moreover, in-network caching, as a key enabling technology of information-centric networking, is brought into UD-LSC networks to achieve both bandwidths saving and delay reduction. However, the multicast capacity is constrained by the signal to interference and noise ratio (SINR) of bottleneck users in each multicast group.Besides, the high-speed movement of LEO satellites makes cache placement and updating more challenging than ground networks with immobile base stations. To track these problems, we first propose a cache-enabled multi-satellite cooperation framework and formulate the multicast transmission problem and the content caching problem. Then, to reduce the average content delivery delay (CDD) of ground users, we propose a cooperative multicast beamforming and transmission-aware dynamic caching (CMB-TDC) algorithm based on the sample average approach and successive convex approximation. Simulation results show that the proposed CMB-TDC algorithm can improve the multicast capacity, and then the average CDD is reduced for UD-LSC networks.

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QoE-Driven Transmission-Aware Cache Placement and Cooperative Beamforming Design in Cloud-RANs

Cited by 37 publications

References 34 publications

Deep Learning for Video Application in Cooperative Vehicle-Infrastructure System: A Comprehensive Survey

Deep Learning for Video Application in Cooperative Vehicle-Infrastructure System: A Comprehensive Survey

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

Cooperative multigroup multicast beamforming for cache‐enabled ultra‐dense low earth orbit satellite constellation networks

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