Quality-of-Experience Oriented Resource Management In Wireless Networks

Wireless data traffic is predicted to increase dramatically in the next few years,up to two orders of magnitude by 2020.This increase is mainly due to multimedia services,enabled by multimedia devices such as tablets and smartphones.A good quality of experience(QoE)would improve customers repeat purchase rate whereas poor QoE would lead to loss of customers.In this case,QoE has becom the key for mobile network operators to improve their profits.Therefore,wireless network operators are now exploring QoE-oriented resource management schemes,which describes the overall performance from the user perspective in order to sustain and to sharpen their competitive edges.However,optimizing QoE in wireless networks is a challenging problem since it must consider many contributing factors such as the application-level characteristics of video(e.g.,video content),the capability of user's device,and the specific wireless network information(e.g.,channel condition,network load).Therefore,the design of efficient QoE-oriented resource management schemes in wireless networks is of great significance for network operators in order to sustain nd refine their competitive edge.In the dissertation,we investigate the QoE-oriented resource management by taking three of the most representative applications in wireless networks,including file downloading,voice,and adaptive bitrate streaming(ABR).Specifically,corresponding the corresponding QoE influencing factors,we design the cross-layer resource manage scheme,develop the near-optimal resource optimization algorithm,and show the algorithm performance for the three appellations,respectively.More specifically,the main contributions of this dissertation are summarized as follows:(1)For file downloading application,we investigate the problem of resource allocation in small cell networks(SCNs)under time-varying wireless channels taking into account individual user's time-averaged rate constraint.Specifically,we employ the concept of virtual queue to model individual user's time-averaged rate constraint.We formulate the optimization problem as a stochastic optimization problem to minimize the long-term time-averaged transmit power of base stations subject to virtual queues stability.We develop a power optimal resource allocation(PORA)algorithm to achieve the optimal power allocation and subcarrier assignment decisions by using Lyapunov optimization technique.Furthermore,considering that the power allocation and subcarrier assignment problem at each time slot is a non-convex combinational problem,we also develop an iterative heuristic algorithm with polynomial complexity.(2)For delay-sensitive applications like voice,we investigate the delay-guaranteed resource management for two-tier small cell networks(SCNs),in which a central macrocell is overlaid with spectrum-sharing small cells.We introduce a cross-tier interference temperature limit to protect MUEs from severe cross-tier interference.We utilize rate control(RC)at the transport-layer and interference-aware resource allocation(RA)(e.g.,joint power allocation and subchannel assignment)at the physical layer to manage the cross-tier interference.We employ stochastic optimization model to maximize the long-term time-averaged capacity of SCNs subject to each SUE's delay constraint,minimum data rate constraint,and the interference temperature limit constraint.We develop a delay-guaranteed capacity optimal algorithm(DCOA)to obtain the optimal RC and RA decisions without prior knowledge of the data arrivals and channel statistics.Particularly,both of the the RC and RA in DCOA have closed-form solutions.(3)For ABR streaming application,we investigate the resource management in orthogonal frequency division multiple access(OFDMA)networks with both time-varying channels and a user's personalized quality requirement.We design a user quality-satisfaction model to evaluate the degree of the user quality satisfaction with respect to his/her personalized quality requirement.We propose a joint rate control associated with the quality adjustment at the application layer,and resource allocation associated with the power allocation and subcarrier assignment at the physical layer to perform the dynamic resource management.By using the Lyapunov optimization technique,we develop a joint rate control and resource allocation(JRCRA)algorithm to maximize the time-averaged quality satisfaction of all users(QSAU).We show that the QSAU achieved by the JRCRA algorithm without any prior knowledge of the channel statistics can arbitrarily attain the optimal QSAU achieved by the algorithm with a complete knowledge of the channel statistics.Simulation results verify the advantages of the proposed JRCRA algorithm...