User Oriented Resource Management with Virtualization: A Hierarchical Game Approach

The explosive advancements in mobile Internet and Internet of Things challenge the network capacity and architecture. The ossification of wireless networks hinders the further evolution towards the fifth generation of mobile communication systems. Ultra-dense small cell networks are considered as a feasible way to meet high-capacity demands. Meanwhile, ultradense small cell network virtualization also exploits an insightful perspective for the evolution because of

Exploiting Interference for Energy Harvesting: A Survey, Research Issues, and Challenges

Interference is one of the fundamental aspects that makes wireless communication challenging, which has attracted great research attention for decades. To solve this interference problem, many interference management (IM) techniques h

Dynamic Quality Adaptation and Bandwidth Allocation for Adaptive Streaming over Time-Varying Wireless Networks

Dynamic adaptive bitrate (ABR) streaming has recently been widely deployed in wireless networks. It, however, does not impose adaptation logic for selecting the quality of video chunks for mobile users. In this paper, we propose a two time-scale resource optimization scheme for ABR streaming over wireless networks under time-varying channels. Our proposed resource optimization scheme takes into account three key factors that make a critical impact on quality of experience (QoE) of ABR streaming, including video quality, quality variation and video rebuffer. Lyapunov optimization technique is employed to maximize the QoE of users by dynamically adapting the video quality at the application layer and allocating bandwidth at the physical layer. Without the prior knowledge of channel statistics, we develop a video streaming algorithm (VSA) to obtain the video quality adaptation and bandwidth allocation decisions. For the arbitrary sample path of channel states, we compare the QoE achieved by VSA with that achieved by an optimal T- slot lookahead algorithm, i.e., knowledge of the future channel path over an interval of length T time slots. Simulation results demonstrate the effectiveness of the proposed VSA for ABR streaming over time-varying wireless networks

Deep Reinforcement Learning (DRL)-Based Transcoder Selection for Blockchain-Enabled Video Streaming

Video transcoding has been widely applied in video streaming industry to convert videos into multiple formats for heterogeneous users. To provide fast and reliable transcoding services, some emerging platforms are leveraging blockchain and smart contract technology to build decentralized transcoding hubs with flexible monetization mechanisms, where any users can rent their idle computing resources to become transcoders in order to receive reward. On these blockchain-enabled platforms, transcoder selection together with resource allocation is a crucial but challenging issue, which should maximize the transcoding revenue to motivate the transcoder candidates as well as handling the dynamic quality of service (QoS) requirements and candidates' characteristics. In this paper, we propose a novel deep reinforcement learning (DRL)-based transcoder selection framework for blockchain-enabled video streaming. Specifically, we propose an evaluation mechanism to facilita

Deep Reinforcement Learning Based Performance Optimization in Blockchain-Enabled Internet of Vehicle

The rapid development of Internet of Vehicles (IoV) necessitates a secure and reliable infrastructure to store and share the massive data. Blockchain, a distributed and immutable ledger, is widely considered as a promising solution to ensure data security and privacy for IoV. To deal with the massive IoV data, the scalability of blockchain becomes a critical issue, which should maximize transactional throughput as well as handling the dynamics of IoV scenarios. Therefore, this paper proposes a novel deep reinforcement learning (DRL) based performance optimization framework for blockchain-enabled IoV, where transactional throughput is maximized while guaranteeing the decentralization, latency and security of the underlying blockchain system. In this framework, we first carry out the performance analysis for blockchain systems from the aspects of scalability, decentralization, latency and security. Further, DRL technique is adopted to selec

Distributed Resource Allocation in Blockchain-based Video Streaming Systems with Mobile Edge Computing

Blockchain-based video streaming systems aim to build decentralized peer-to-peer networks with flexible monetization mechanisms for video streaming services. On these blockchain-based platforms, video transcoding, which is computational intensive and time consuming, is still a major challenge. Meanwhile, the block size of the underlying blockchain has significant impacts on the system performance. Therefore, this paper proposes a novel blockchain-based framework with adaptive block size for video streaming with mobile edge computing (MEC). First, we design an incentive mechanism to facilitate the collaborations among content creators, video transcoders and consumers. In addition, we present a block size adaptation scheme for blockchain-based video streaming. Moreover, we consider two offloading modes, i.e., offloading to the nearby MEC nod

A Deep Reinforcement Learning-Based Transcoder Selection Framework for Blockchain-Enabled Wireless D2D Transcoding

The boom of video streaming industry has resulted in the increasing demands for transcoding services from heterogeneous users. Recent advances of blockchain technology allow some startups to realize decentralized collaborative transcoding through device-to-device (D2D) networks, where a group of transcoders are selected to perform transcoding cooperatively. For the blockchain-enabled D2D transcoding systems, it's imperative to jointly design transcoder selection, task scheduling and resource allocation schemes in order to provide efficient and trustworthy transcoding services. In this paper, viewing the involved multi-dimensional complex factors and channel fluctuation, we propose a novel deep reinforcement learning (DRL) based transcoder selection framework for blockchain enabled D2D transcoding systems where both the platform dynamics and channel statistics are captured. To reduce the action space size, we adopt a two-stage decision approach to first select the transcoders through a normal DRL based framework and then obtain the optimal task scheduling, power control, and resource allocation scheme by solving a stochastic optimization problem with t

Computation Offloading and Content Caching in Wireless Blockchain Networks with Mobile Edge Computing

Blockchain technology has been applied in a variety of fields due to its capability of establishing trust in a decentralized fashion. However, the application of blockchain in wireless mobile networks is hindered by a major challenge brought by the proof-of-work (PoW) puzzle during the mining process, which sets a high demand for the computational capability and storage availability in mobile devices. To address this problem, we propose a novel mobile edge computing (MEC) enabled wireless blockchain framework where the computation-intensive mining tasks can be offloaded to nearby edge computing nodes and the cryptographic hashes of blocks can be cached in the MEC server. Particularly, two offloading modes are considered, i.e., offloaded to the nearby access point (AP) or a group of nearby users. First, we conduct the performance analysis of each mode with stochastic geometry methods. Then, the joint offloading decision and caching strategy is formulated as an optimization problem. Furthermore, an alternating direction method of multipliers (ADMM) based algorithm is utilized to solve the problem in a distributed manner. Fin