On accounting for screen resolution in adaptive video streaming: QoE-driven bandwidth sharing framework

Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link mean opinion score (MOS) to video bitrate for different screen types (e.g., Common Intermediate Format [CIF], Quarter Common Intermediate Format [QCIF], and High Definition [HD]). We leverage such studies and formulate a Quality of Experience (QoE)-driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and Karush Kuhn Tucker (KKT) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look-alike strategy implementing max-min fairness.     

A distributed pricing algorithm for achieving network‐wide proportional fairness

Proportional fairness (PF) scheduling achieves a balanced tradeoff between throughput and fairness and has attracted great attention recently. However, most previous work on PF only considers the single cell scenario. This paper focuses on the problem of achieving network‐wide PF in a generalized multiple base station multiple user network. The problem is formulated as a maximization model and solved using the dual method. By decomposing the dual objective function, we get a distributed pricing based algorithm. Optimality of this algorithm is presented. Although the algorithm is derived using fixed link rate assumption, it can still apply in the presence of time‐varying rates. The proposed algorithm is suitable for distributed systems in the sense that it does not need any inter base station communication at all. Simulations illustrate that the proposed distributed network‐wide PF scheduling algorithm achieves almost the same performance as the centralized one. Compared with traditional local PF (LPF) scheduling, the network‐wide PF scheduling achieves higher throughput, lower throughput oscillation, and greater fairness. Copyright © 2010 John Wiley & Sons, Ltd.     

MIMO Scheduling Effectiveness Analysis for Bursty Data Service from View of QoE

In the user selection phrase of the conventional Multiple-input-multiple-output (MIMO) scheduling schemes,the frequent user exchange deteriorates the Quality of user experience (QoE) of the bursty data service.And the channel vector orthogonalization computation results in a high time cost.To address these problems,we propose an inertial scheduling policy to reduce the number of noneffective user exchange,and substitute self-organization policy for channel vector orthogonalization computation to reduce computational complexity.The relationship between the scheduling effectiveness and the inertia of objective function is observed in the simulation.The simulation results show that the inertial scheduling policy effectively reduce the number of potential noneffective scheduling which is inversely proportional to the Mean opinion score (MOS) that quantifies the QoE.Our proposed scheduling scheme provides significant improvement in QoE performance in the simulation.Although the proposed scheduling scheme does not consider the channel vector orthogonalization in the user selection phrase,its throughput approaches the level of the throughput-oriented scheme because of its selforganization scheduling policy.     

移动自组织网络中基于QoE的跨层多波束信道接入机制

引入数字多波束技术可以使移动自组织网络的信道容量得到有效提升，然而，时间、频率、空间、用户、功率等多维度资源域的统一调度与跨层联合优化使得信道接入机制的分析与设计变得更加复杂。为此，结合多波束的特性，将波束宽度限制、收发限制、通道限制、功率限制等多种约束条件抽象图论问题，并建立分析模型。基于分析模型，进一步提出了MB-MAC（Multi-beam Medium Access Control）信道接入机制，结合物理层的预编码技术，建立定向链路的队列模型，度量用户的满意度(Quality of Experience,QoE)，设计了适用于自组织网络中多用户并发并收的信道接入机制，兼顾了网络容量和用户满意度。仿真结果表明，MB-MAC机制能在保证各个用户满意度的情况下，有效提升网络吞吐率。     

基于用户体验的VLC/WiFi异构网络切换算法

针对可见光通信(VLC)与WiFi组成的异构网络中的网络选择问题,现有工作大多仅关注实际物理网络指标的优化,如信干噪比(SINR)和吞吐量等,而忽视了终端用户的异构需求和服务体验。基于此,该文提出了一种以用户体验质量(QoE)为优化目标的网络切换算法。该算法首先基于平均主观评分(MOS)机制建立离散化的QoE等级模型,然后根据不同网络对应的实际QoE水平给出了相应网络切换准则,当且仅当新网络使得用户QoE等级提升时才进行网络切换,从而在一定程度减小盲目追求容量优化引起的网络切换开销。仿真结果表明,该算法相比现有算法能够在保证QoE水平的前提下显著减小整体的网络切换次数,从而降低网络开销,提升网络性能。      

Scheduling schemes for multimedia service in wireless OFDM systems

Scheduling schemes play a key role in the system performance of broadband wireless systems such as WLANs/WMANs. Maximal SNR and round robin are two conventional scheduling strategies that emphasize efficiency and fairness, respectively. The proportional fair scheme provides a trade-off between efficiency and fairness, and has been well studied in TDMA and CDMA systems. In this article we extended the PF scheduling scheme to OFDM-based BWSs (OPF). In addition, we propose three variations: adaptive OPF (AOPF), multimedia AOPF (MAOPF), and normalized MAOPF (NMAOPF) in order to meet the QoS requirements for multirate services in multimedia systems. The adaptive modulation and coding schemes in time varying and frequency selective fading are considered. The system performances of the algorithms are compared in terms of efficiency (throughput and mean packet delay) and fairness (user satisfaction rate and average user rate). Joint physical and media access control layer simulation results show that AOPF and MAOPF can improve throughput at the cost of fairness, and NMAOPF can provide the highest throughput without losing fairness.     

流量业务用户感知的评估方法分析

本文探寻移动数据业务网络参数、性能指标与用户体验之间的关系,分析影响用户感知的因素,提出用现网数据量化用户体验的方法。对于移动数据业务,我们将从移动用户体验,即感知质量QoE的角度出发,分析现网数据业务特征,研究影响业务QoE关键因素,抽象数据业务类型,探索评估各类业务用户感知质量评估的理论依据。     

User scheduling and power allocation for nonfull buffer traffic in NOMA downlink systems

Nonorthogonal multiple access (NOMA) is one of the key technologies for 5G, where the system capacity can be increased by allowing simultaneous transmission of multiple users at the same radio resource. The most of the proportional fairness (PF)–based resource allocation studies for NOMA systems assumes full buffer traffic model, while the traffic in real‐life scenarios is generally nonfull buffer. In this paper, we propose User Demand–Based Proportional Fairness (UDB‐PF) and Proportional User Satisfaction Fairness (PUSF) algorithms for user scheduling and power allocation in NOMA downlink systems when traffic demands of the users are limited and time‐varying. UDB‐PF extends the PF‐based scheduling by allocating optimum power levels towards satisfying the traffic demand constraints of user pair in each resource block. The objective of PUSF is to maximize the network‐wide user satisfaction by allocating sufficient frequency and power resources according to traffic demands of the users. In both cases, user groups are selected first to simultaneously transmit their signals at the same frequency resource, while the optimal transmission power level is assigned to each user to optimize the underlying objective function. In addition, the genetic algorithm (GA) approach is employed for user group selection to reduce the computational complexity. When the user traffic rate requirements change rapidly over time, UDB‐PF yields better sum rate (throughput) while PUSF provides better network‐wide user satisfaction results compared with the PF‐based user scheduling. We also observed that the GA‐based user group selection significantly reduced the computational load while achieving the comparable results of the exhaustive search.     

A fair optimization scheduling scheme for IEEE 802.16 networks in multimedia applications

IEEE 802.16 networks are designed based on differentiated services concept to provide better Quality of Service (QoS) support for a wide range of applications, from multimedia to typical web services, and therefore they require a fair and efficient scheduling scheme. However, this issue is not addressed in the standard. In this paper we present a new fair scheduling scheme which fulfills the negotiated QoS parameters of different connections while providing fairness among the connections of each class of service. This scheme models scheduling as a knapsack problem, where a fairness parameter reflecting the specific requirements of the connections is defined to be used in the optimization criterion. The proposed scheduler is evaluated through simulation in terms of delay, throughput and fairness index. The results show fairness of the scheduling scheme to all connections while the network guarantees for those connections are fulfilled.     

QoE Model Based Optimization for Streaming Media Service Considering Equipment and Environment Factors

With the development of smart phones, one of the hot research topics for wireless personal communication technologies has begun to transit from reaching higher communication capabilities to better Quality of Experiences (QoE). However, for streaming services, such as online music or online video, which is one of the mainstream services in the future wireless network, there is still no breakthrough optimization beyond the Quality of Service (QoS) hence the optimization results are not satisfactory. Based on our study, this problem could be relieved by altering the criterion of wireless resource allocation from enhancing users?? QoS to enhancing their QoE, which specifically means that the scope of the research is extended from the so-called last mile between the network and the user equipment to the last few inches between the user equipment and the user himself. Unfortunately, it seems that all current QoE researches are still not jointly considering the users?? environment and user equipments?? performance, both of which are paramount factors of the last inches that will influence users?? QoE directly and profoundly. Hence, in this paper, we propose a QoE model involving the environment and equipment factors, and then we use this model to guide the wireless resource allocation. Simulation results show that this resource allocation method will increase the effectiveness of network resource utilization as well as improve the average of satisfaction of users.