A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> With widespread use of multimedia communication, quality of experience (QoE) progressively becomes an important factor in networking today. Besides, multimedia applications can be supported under various technologies, including wired and wireless networks. Among them, Universal Mobile Telecommunications System (UMTS) is one of the most popular technology thanks to its support on mobility. Improved with a new access method (High Speed Downlink Packet Access or HSDPA), it can provide higher bandwidth and enable wider range of services including multimedia applications. In UMTS, different categories of traffic are specified along with their characteristics. Best effort traffic has been specified with less priority because it has fewer constraints. On the other hand, real-time multimedia traffic such as streaming video or VoIP are more sensitive to network condition changes, hence special treatment (e.g. QoS scheduler) is needed in order to achieve user satisfaction. According to the literature, most of scheduling mechanisms mainly take into account signal quality and fairness and do not consider user perception. In this paper, we propose a novel approach, QoE-aware scheduler that takes quality of experience into account when making scheduling decisions. Compared to other existing schedulers, QoE-aware approach has reached a profitable performance in terms of user satisfaction, throughput, and fairness. <s> BIB001 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> The provision of high speed access to Internet and IP-based services is one of the main goals of beyond 3G (B3G) wireless systems. These systems will benefit from cross-layer protocol designs that will introduce interactions between different layers to obtain performance gains. The majority of the research in the field of cross-layer in B3G systems aims at improving quality of service (QoS) system centric metrics such as spectral efficiency, service latency, delay variation (jitter), etc. However, minor attention has been paid to the satisfaction of the subjective quality requirements from human users. With the goal of incorporating the subjective human perception into the cross-layer design of B3G systems, this work carries out an experimental survey of the sensitivity of the user subjective quality to the service response time for the Web browsing application. From the experimental results, a mapping from service response time and user data rate (provided by the wireless link) to mean opinion score (MOS) is derived. The presented results will show that the Web page size plays an important role in the mapping function. The derived mapping function is incorporated into a radio resource allocation algorithm for orthogonal frequency division multiple access (OFDMA) systems. This incorporation is carried out maximizing the aggregate utility over all the users in the cell. Its performance has been compared to that of the multicarrier proportional fair (MPF) under heavy load conditions with a 3G LTE simulator. The results have shown that the proposed methodology can provide an interesting enhancement of the user experienced quality compared to the MPF algorithm. <s> BIB002 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> In this paper, we apply a Quality of Experience (QoE) based Cross-Layer Optimization (CLO) framework for efficiently allocating the network resources for video delivery in LTE mobile networks. Conventionally, the objective function aims to maximize the average user-perceived quality of all users by jointly optimizing the application layer and the lower layers of the radio communication system. We extend the conventional objective function by a new constraint that allows the network operator to set its policy to provide the video service with a target average video quality. The Mean Opinion Score (MOS) is used to indicate the user satisfaction of the video service. A comparison between our approach, an Application-Layer Rate Adaptation (ALRA) and a non-optimized LTE system is done by performing simulations using an OPNET LTE simulator. Results show that our QoE-based approaches lead to significant improvements of user-perceived quality, and additionally to significant bandwidth savings in the case of target mean MOS approach. <s> BIB003 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> We address a multi-criteria Quality of Experience (QoE) driven optimization problem for multi-user wireless video delivery. The optimization is done in a cross-layer fashion by using parameterized models of application and link layer in order to determine the application data rate and the network resources that fulfill the criteria. Mean Opinion Score (MOS) is used as a unified utility metric that encompasses the user-perceived quality under certain receiving conditions for the user application. We consider two partially contrary objectives, which are maximizing the total perceived quality of the whole system and maximizing the utility fairness among all users. A tuning mechanism is proposed, which allows the network operator to change dynamically its operating point of the resource allocation based on its pre-defined policy of any combinations of these two objectives. We implemented the proposed tuning algorithm in an emulated Long-Term Evolution (LTE) system and verified its feasibility through simulations. <s> BIB004 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> Since the Quality of Experience (QoE) metric directly reflects the subjective experiences of users, the resource allocation techniques with the objective to improve QoE instead of conventional metrics are efficient in resource utilization. This kind of techniques can avoid wasting resources in providing perfect objective metric which may have little or even no effect on user experience. This paper presents a QoE-aware power allocation algorithm in multiuser Orthogonal Frequency Division Multiplexing (OFDM) systems. The power allocation problem is formulated to maximize the overall QoE subject to the total transmit power constraint. The original model is solved by means of transformation and approximation, since it is difficult to develop a closed-form solution. Simulation results show that the proposed algorithm can enhance the performance in terms of both the overall QoE and single user QoE in comparison with the classical water-filling algorithm. <s> BIB005 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> Video traffic contributes to the majority of data packets transported over cellular wireless. Future broadband wireless access networks based on 3GPP's Long Term Evolution offer mechanisms for optimized transmission with high data rates and low delay. However, especially when packets are transmitted in the LTE downlink and if services are run over-the-top (OTT), optimization of radio resources in a multi-user environment for video services becomes infeasible. The current market trend is moving to OTT solutions, also for video transmission, where an emerging standard based on HTTP streaming - DASH - is expected to have a huge success in the upcoming years. The solution presented in this paper consists of a novel technique, which combines LTE features with knowledge on DASH sessions for optimization of the wireless resources. The combined optimization yields an improved transmission of videos over cellular wireless systems which are based on LTE and LTE-Advanced. <s> BIB006 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> In this paper, a MOS-driven energy efficient power allocation scheme (MEEPA) is proposed for video transmission over downlink orthogonal frequency division multiple access (OFDMA) networks. In the proposed framework, system power consumption and MOS for users based on end-to-end video distortion have been jointly optimized. We also define a novel energy efficient utility as the unifying metric based on the MOS model and the system power consumption. The main objective is to maximize the energy efficiency by balancing the MOS and the power consumption for each user. The power allocation problem is solved by using constrained particle swarm optimization (CPSO). Moreover, simulation results show that the proposed algorithm can improve energy efficiency greatly in terms of lower power consumption with a comparable video quality performance compared with the traditional algorithm. <s> BIB007 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> This paper proposes a novel approach of cross-layer resource allocation based on Quality of Experience (QoE) for wireless video transmission. We model and formulate the cross-layer resource allocation problem in accordance with the relationship between transmission bit rate and QoE. Our objective is to ameliorate the system QoE level while guaranteeing the fairness for users. The video quality fluctuation that affects the system QoE is considered as well. The proposed algorithm designs an adaptive resource allocation approach depending on limited network resources. We use a downlink LTE system as a simulation example of a high speed downlink access system. The simulation results demonstrate that the proposed method considerably and agreeably improves system QoE more than other algorithms. <s> BIB008 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> We model the user's quality of experience (QoE) in a wireless voice over IP (VoIP) service as a function of the amount of effort the user has to put to continue her conversation. We assume that users would quit or terminate an ongoing call if they have to put more efforts than they could tolerate. Not knowing the tolerance threshold of each individual user, the service provider faces a decision dilemma of whether to fix the network problem immediately whenever he detects a user effort in the VoIP system, or ignore it with the hope that the user may still continue the call anyway. In this paper, we formulate the provider's dilemma as a non-cooperative game between the provider and the VoIP user experiencing a deteriorating QoE. We demonstrate that providers implementing the equilibrium solutions can expect to not only increase their revenues, but also reduce the number of cases when users quit out of frustration thus minimizing potential churning. We also discuss conditions under which a sophisticated user may or may not benefit from faking unwarranted efforts with a goal of receiving a better service from the provider. Finally, we conduct a subjective experiment of VoIP over WiFi, which verifies the key model assumption that perceptual quality is negatively correlated to the amount of effort the user has to put to continue the call. <s> BIB009 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> Quality of Experience (QoE) is a widely used metric reflecting the users' satisfaction of the multimedia service. This paper studies the QoE-driven resource allocation technique in the Orthogonal Frequency Division Multiplexing (OFDM) system. Different from other schemes, multiuser-multiservice scenario is taken into consideration for both power allocation and subcarrier scheduling, which is much closer to the practice communication system. A gradual resource allocation algorithm (GRAA) with lower computational complexity is proposed, which aims at addressing the system energy efficiency while guaranteeing user-perceived QoE for different multimedia services. Simulation results show that the almost same performance can be approached by our proposed scheme with much lower complexity compared with the exhaustive search algorithm. <s> BIB010 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> Carrier aggregation is one of the key techniques for the advancement of long-term evolution (LTE- Advanced) networks. This article proposes a quality- of-experience (QoE)-based carrier scheduling scheme for networks with multiple services. The proposed scheme aims at maximizing the user QoE, which is determined by both the application-level and network-level quality of services. Packet delay, as an essential factor affecting QoE, is first discussed under the context of QoE optimization as well as the data rate. The component carriers are dynamically scheduled according to the network traffic load by the proposed novel scheme. Simulation results show that our approach can achieve significant improvement in QoE and fairness over conventional approaches. <s> BIB011 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> With the soaring demands for high speed data communication, as well as transmission of various types of services with different requirements over cellular networks, having a decent radio resource management is considered vital in Long Term Evolution (LTE) system. In particular, satisfying the Quality of Service (QoS) requirements of different applications is one of the key challenges of radio resource management that needs to be dealt by the LTE system. In this paper, we propose a cross-layer design scheme that jointly optimizes three different layers of wireless protocol stack, namely application, Medium Access Control (MAC), and physical layer. The cross-layer optimization framework provides efficient allocation of wireless resources across different types of applications (i.e., real-time and non real-time) run by different users to maximize network resource utilization and user-perceived quality of service, or also known as Quality of Experience (QoE). Here, Mean Opinion Score (MOS) is used as a unified QoE metric that indicates the user-perceived quality for real-time or multimedia services notably video applications. Along with multimedia services, the proposed framework also takes care of non-real-time traffic by ensuring certain level of fairness. Our simulation, applied to scenarios where users simultaneously run different types of applications, confirms that the proposed QoE-oriented cross-layer framework leads to significant improvement in terms of maximizing user-perceived quality as well as maintaining fairness among users. <s> BIB012 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> HTTP progressive video downloading is becoming increasingly relevant in wireless and mobile networks. Therefore, improving the quality of experience (QoE) for this service is important for customer satisfaction. In this letter we propose a QoE-aware scheduling solution for the HTTP progressive video downloading in OFDMA systems. Whereas most scheduling strategies consider QoS metrics such as delay, jitter or throughput, our proposal takes into account a metric that directly affects the end-users experience. In particular, we propose to use an estimation of the amount of video data stored in the player buffer as the main criterion for resource assignment. The ultimate goal of using this criterion is the avoidance of playback interruptions. In particular, we have evaluated this scheduler in an LTE network simulator for the YouTube service, which is the most visited HTTP video streaming site. Our results show a significant improvement compared to the Multicarrier Proportional Fair scheduler, e.g. the number of videos with pauses decrease from 16.1% to 0% for 12 Mbps of cell offered load. <s> BIB013 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> A real-time adaptive resource allocation algorithm considering the end user's Quality of Experience (QoE) in the context of video streaming service is presented in this work. An objective no-reference quality metric, namely Pause Intensity (PI), is used to control the priority of resource allocation to users during the scheduling process. An online adjustment has been introduced to adaptively set the scheduler's parameter and maintain a desired trade-off between fairness and efficiency. The correlation between the data rates (i.e. Video code rates) demanded by users and the data rates allocated by the scheduler is taken into account as well. The final allocated rates are determined based on the channel status, the distribution of PI values among users, and the scheduling policy adopted. Furthermore, since the user's capability varies as the environment conditions change, the rate adaptation mechanism for video streaming is considered and its interaction with the scheduling process under the same PI metric is studied. The feasibility of implementing this algorithm is examined and the result is compared with the most commonly existing scheduling methods. <s> BIB014 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> In this paper, a scheduling algorithm which allocates resources based on the QoE (Quality of Experience) of users for streaming video applications has been proposed. The proposed algorithm, referred to as Delay Constrained Proportional Fairness (DCPF), minimizes the inter-frame delay for a streaming video application by prioritizing users based on the difference between the delay of the head of the line packet and the average delay experienced by the user. It is observed that the proposed algorithm reduces inter-frame delay. The performance evaluation has been carried out on NS-3 over an LTE network using the Evalvid video evaluation tool. The algorithm has been tested for different mobility scenarios for videos with varying user speeds. It is seen that the proposed algorithm provides up to 23% reduction in the video frame delay when compared to the existing Proportional Fairness (PF) algorithm. <s> BIB015 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> The prevalence of broadband wireless networks offering nearly ubiquitous Internet connectivity today has opened new possibilities for utilizing wireless and mobile services also in eHealth applications. However, such services often involve transmission of critical multimedia data, thus having stringent requirements for network Quality of Service (QoS) not supported in current networks. In this paper, we propose means for implementing enhanced QoS support for medical video transmission in wireless networks. Our solution extends existing wireless network QoS architectures with support for differentiating multiple flows and packets of the same service type based on their impact on the Quality of Experience (QoE). The paper also presents an experimental evaluation of the solution in a simulation environment. The simulation results indicate that our solution enhances the QoE significantly and therefore improves the availability of critical medical video services. <s> BIB016 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> Instant Messaging (IM) service has drawn growing attention in current wireless networks. However, the Quality of Experience (QoE) of IM service users are instable, since the IM service is not served with dedicated channel or guaranteed bandwidth. In this paper, we adopt a Representative Service (RS) based QoE model for IM service, and propose a scheduling algorithm to provide fair QoE to IM service users. In the QoE model, the subservice that a user is currently focusing on is defined as the RS and the quality of RS, Representative Service Quality (RSQ), is considered as the QoE of an IM service user. In our scheduling algorithm, RS is estimated when several subservices are launched simultaneously, and resource allocation is done accordingly to avoid the QoE degradation due to resource insufficiency. Simulation results validate the rationality and effectiveness of the proposed algorithm, show that it outperforms classical fair scheduling algorithms. <s> BIB017 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> Quality of Experience (QoE) is a widely used metric to measure the perceptive feeling of end users for multimedia service. In this paper, a QoE-driven radio resource allocation scheme has been developed for the multiuser-multiservice OFDM system. Different from the previous schemes, we take the service type into consideration when allocating radio resource. Three multimedia applications have been discussed in this work, including Voice over Internet Protocol (VoIP), Internet Protocol Television (IPTV) and File Download (FD). For all the multimedia applications, their QoE are related to the scheduled radio resource, the objective of our scheduling scheme is to maximize the overall QoE while guaranteeing all the users can perceive a relatively good QoE level. The proposed scheduling algorithm can be divided into two parts: Firstly, allocate subcarriers to all the users in order to guarantee their minimal transmit rate requirement, then the remaining subcarriers are allocated to the users who can achieve the best QoE gain. The simulation results indicate that our algorithm can reach high overall QoE performance while maintaining an acceptable fairness in the system. <s> BIB018 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> The assurance of quality of experience (QoE) and provisioning of high throughput of the system represent the main goals of future wireless and mobile networks. This paper presents novel and practical cross-layer QoE-aware radio resource allocation (RRA) algorithms for the downlink of a heterogeneous orthogonal frequency division multiple access (OFDMA) system. The objective of the proposed algorithms is to assure the appropriate level of QoE for each user of the system by incorporating application-layer parameters and subjective human perception of quality into the RRA process. We propose two user-oriented joint subcarrier and power allocation algorithms with low complexity for real-time and interactive services. The first algorithm dynamically allocates resources by assuring the same level of QoE to all users of the system, whereas the second algorithm introduces the efficient trade-off between the user's QoE and the spectral efficiency of the system. By considering application-layer parameters and user's perception of quality, high users' QoE and explicit control of data rates can be achieved. The numerical results show that the proposed algorithms achieve significant increase in the level of QoE compared to previous works, a fair distribution of capacity among users and near to optimal solution of QoE for the OFDMA system. <s> BIB019 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> The long term evolution (LTE) is a breakthrough technology with respect to the previous generation of cellular networks, as it is based on an all-IP architecture that aims at supporting several high quality services, such as video streaming, voice-over-internet-protocol (VoIP) and everything related to the wideband Internet access. In the recent years, VOIP service is becoming more and more popular and important, while the tight delay requirements and the scarcity of radio resources problem remain the main challenges for it. To solve these problems, we construct a mathematical model and design a novel algorithm for the scheduling problem of VOIP. Limiting the delay, our algorithm solve the scarcity of radio resources problem by minimizing the total number of radio resources scheduled during a certain period of time, which can ensure the quality-of-experience (QOE) of user equipment (UE) and high resource utilization rate. Moreover, the maximum delay of a UE with a certain average transmitting rate is determined theoretically. Finally, we give simulation results of our scheduling algorithm and the comparison with the modified largest weighted delay first (M-LWDF) and the exponential/proportional fair (EXP/PF) algorithms, by which we demonstrate the effectiveness of our algorithm on delay and resource utilization. <s> BIB020 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> This paper proposes a QoE-driven LTE downlink scheduling algorithm which is able to maximise the number of users per cell while maintaining an acceptable QoE for VoIP applications. The QoE-driven scheduling algorithm was implemented in LTE-Sim and its performance was compared to common LTE scheduling algorithms. The main difference between QoE-driven scheduling and existing scheduling mechanisms was prioritisation of users for resource allocation by taking into account their QoE requirements for VoIP applications. Preliminary results have shown that QoE-driven LTE downlink scheduling improved the cell capacity by 75% compared to MLWDF and 250% compared to PF and EXP-PF. <s> BIB021 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> In this letter, we focus on a quality of experience (QoE) aware resource allocation that considers users' time varying channel status and heterogeneous service requirements in multi-cell OFDMA networks. We propose a QoE-based proportional fair (PF) scheduling considering network-wide users' QoE maximization as well as fairness among users. Our utility function is based on the mean opinion score (MOS) model reflecting the application-specific characteristics. We introduce a Bezier curve based continuously differentiable MOS model instead of using the conventional bounded logarithmic MOS model. Then, we propose a utility function, which is proven to be concave, for QoE provision to achieve the global optimality with opportunistic gradient scheduling. Our proposed technique is shown to be easily incorporated with inter-cell interference coordination (ICIC). Simulation results show that the proposed QoE-aware PF scheme significantly improves the network-wide users' average QoE as well as the edge users' performance, e.g., the 5th percentile QoE. <s> BIB022 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> We consider in this paper an important Quality of Experience (QoE) indicator in cellular networks that is reneging of users due to impatience. We specifically consider a cell under heavy load conditions, modeled as a multiclass Processor Sharing system, and compute the reneging probability by using a fluid limit analysis. In order to enhance the user QoE, we propose a radio resource allocation control scheme that minimizes the global reneging rates. This control scheme is based on the a-fair scheduling framework and adapts the scheduler parameter depending on the traffic load. While the proposed scheme is simple, our results show that it achieves important performance gains. <s> BIB023 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> This work focuses on an analytical investigation a performance of cellular networks for transmission video content. Degradation of video playback is rising, due to increasing a video traffic amount in the modern cellular networks. Stalling of a video stream has the greatest influence to user's personal contentment, which is possible to estimate by rebuffering percentage (percentage of watching time in which user experience is stalling effects). Resource allocation algorithms upgrade at a base station makes possible to decrease degradation effects for video streams. In this article, we propose a lower bound for a mean user's rebuffering percentage of HTTP Progressive Download traffic type in cellular networks in all possible schedulers and optimal scheduling strategy based on it. The performance of the proposed scheduler demonstrates by Key Performance Indicators for video streaming (QoE: Mean Opinion Score and rebuffering percentage) and network (Throughput and Fairness). The obtained results can use as a baseline for performance estimation of existing and new scheduling algorithms for video traffic transmission over cellular networks. <s> BIB024 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> The scarcity of the available radio spectrum coupled with the growing popularity of bandwidth intensive mobile video applications poses a huge challenge to network operators. The solution of over-provisioning the network is not economical; hence, an appropriate strategy for scheduling and resource allocation among the users in the system is of crucial importance. This work focuses on scheduling multiple video flows on the downlink of a wireless system based on orthogonal frequency division multiple access (OFDMA), such as Long-Term Evolution (LTE) and LTE-A (LTE-Advanced) standards. We propose a joint multi-user scheduling and multi-user rate adaptation strategy providing an appropriate trade-off between efficiency and fairness, while ensuring high quality of experience (QoE) for the end users. We consider Scalable Video Coding (SVC) which facilitates the truncation of bit streams, thus allowing graceful degradation of video quality in the event of wireless channel variations or network congestion. The proposed scheduler utilizes QoE-aware priority marking, where video layers are mapped to priority classes and targets at minimizing delay bound violations for the most important priority classes under congestion. In order to reduce congestion, we propose multi-user rate adaptation at the MAC layer via a novel dynamic filtering policy for QoE-based priority classes. Simulation results show that the proposed approach delivers to the end users a similar QoE as delivered by the state-of-the-art cross-layer approaches, where extensive cross-layer signaling, additional video rate adaptation modules at the core network, and frequent link probing from the wireless access network to the rate adaptation modules are required. The latter approaches are not implemented in real systems due to the aforementioned drawbacks, while our approach can be implemented without major modifications in the standard behavior of existing networks and equipment. The proposed framework can deliver delay-sensitive traffic as well as delay-tolerant best-effort traffic. <s> BIB025 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> The provision of Quality of Experience (QoE) for wireless video streaming has become a necessity. From this perspective, we design a QoE-aware scheduling (QoEAS) scheme for video streaming over IEEE 802.11n/ac networks. We consider an application scenario where videos are streamed in an area with high density of users. To improve the streamed video quality, we enhance the conventional gradient based scheduling scheme (U'R) by incorporating the packet delay and channel transmission rate with video packet importance index in scheduling. This index is derived based on the gradient of the QoE function for video, which accurately reflects the usefulness of a packet in enhancing the video quality. To be compatible to practical networks such as IEEE 802.11n/ac, the packet importance index is embedded in the Type-of-Service (ToS) field of IP header and is employed at Media Access Control (MAC) layer to perform scheduling. The proposed QoEAS significantly enhances the streamed video quality and achieves good fairness among users. It surpasses the conventional scheme by as much as 5 dB in Peak Signal-to-Noise Ratio (PSNR) at the lower 10 percentile of the users. To demonstrate the superiority and practicability of QoEAS, we also set up a lab test bed on a real 802.11ac network. Compared to conventional schemes, the proposed QoEAS shows notable QoE enhancement in lab test. <s> BIB026 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> In this paper, we implement an application-aware scheduler that differentiates users running real-time applications and delay-tolerant applications while allocating resources. This approach ensures that the priority is given to real-time applications over delay-tolerant applications. In our system model, we include realistic channel effects of Long Term Evolution (LTE) system. Our application-aware scheduler runs in two stages, the first stage is resource block allocation and the second stage is power allocation. In the optimal solution of resource block allocation problem, each user is inherently guaranteed a minimum Quality of Experience (QoE) while ensuring priority given to users with real-time applications. In the power allocation problem, a new power allocation method is proposed which utilizes the optimal solution of the application-aware resource block scheduling problem. As a proof of concept, we run a simulation comparison between a conventional proportional fairness scheduler and the application-aware scheduler. The simulation results show better QoE with the application-aware scheduler. <s> BIB027 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> Radio resources in Long Term Evolution (LTE) systems are distributed among User Equipments (UE) without considering the application and content being transmitted. When the radio capacity is fully utilized, the bearer based service strategy aiming to maximize the spectral efficiency may not support good customer experience for popular Over The Top (OTT) applications where each session has an individual and highly dynamic bandwidth demand. In this paper, we argue that maximizing the system efficiency cannot be decoupled from customer experience, therefore maximized spectral efficiency does not necessarily improve the service quality perception of end users. Furthermore, we show that the existing LTE QoS architecture does not bring sufficient benefit on this matter and conclude that customer experience is a better system level target. Such operation requires a different service paradigm where resources are allocated to individual application sessions adaptively and dynamically by understanding their specific requirements. We propose a Quality of Experience (QoE) Manager that can deliver superior QoE for each session by loosening the radio efficiency targets to maximize the customer experience. Still, the radio scheduler retains sufficient freedom to optimize on channel diversity. The solution is demonstrated via simulations including the successful management of encrypted YouTube downloads. <s> BIB028 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> Recently, multimedia communication by smart-phones and tablets via mobile networks have become popular in daily life. Therefore, an effective method to use the limited wireless resources is necessary. When using wireless resources among multiple users, the schedulers allocate resources to each user. When allocating resources, assuring quality of experience (QoE) and achieving high system throughput are fundamental. In this paper, we present a two-step radio resource allocation algorithm for the downlink of a long term evolution (LTE) network, by taking into account the user's and the service provider's perspective. At the first step, it allocates resources dynamically to each user so that a minimum QoE is guaranteed for every user. In the second step, it allocates resources dynamically to improve the system throughput while achieving a high-level of QoE. We show that our proposed algorithm achieves a higher level of QoE than the conventional allocation algorithms based on quality of service (QoS) metrics, as well as a higher throughput compared to the conventional allocation algorithms based on QoE. <s> BIB029 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> Radio scheduling is a well-studied problem that has challenged researchers throughout the last decades. However, recent findings that stem from the Quality of Experience (QoE) domain come to give a new perspective to traditional radio scheduling approaches. In this paper, we take advantage of recent subjective results regarding the impact of throughput fluctuations on the QoE of interactive applications and revisit well-known scheduling algorithms. By quantifying the impact of traditional radio schedulers on user-perceived QoE, we manage to draw new conclusions regarding the radio scheduling problem, such as the importance and impact of consistency of the resource allocation decisions on the users' QoE. As main result, fair algorithms inherently seem to be more consistent than greedy ones, providing less throughput fluctuations and, thus, better QoE. Based on this outcome, we propose a new scheduling approach, which further improves end- users' QoE by moderating throughput fluctuations. <s> BIB030 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> A. Passive End-user Device Strategies <s> This paper proposes online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems, where an access point transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON–OFF channels plus constant-bit-rate videos and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels and variable-bit-rate videos, and we prove that it remains optimal under some mild conditions. We then formulate a network utility maximization problem based on the QoE of each flow. We demonstrate that our policies achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation and experimental results validate that the proposed policies indeed outperform existing policies. <s> BIB031
Scheduling techniques are easier to implement in a wireless network when the users, as well as their devices, do not perform any exclusive QoE tasks (e.g., monitoring, measuring and reporting relevant parameters), as the QoE assessment is based on measurements that can be carried out solely at the base station side. Since the required assessments can be performed by the scheduler on the network side, no extra information needs to be exchanged between the user's device and the network. On the other hand, these approaches may not achieve the best possible QoE performance, since many relevant metrics, which could be collected at the end-user device (e.g., buffer status), either cannot be used or have to be estimated. 1) Video Streaming: The simplest QoE-based scheduling approaches consider only one relevant parameter within the the QoE optimization process; examples of these approaches can be found in BIB003 - BIB012 . The works described in BIB003 , BIB004 deal with video delivery over LTE networks and make use of utility functions, which are based on the required transmission rate at the application-layer in order to achieve a certain level of QoE. In BIB003 , the authors propose a framework that allows network operators to set their policy for providing service with a certain level of mean quality of all users, in order to save some network resources, which could be used to serve more users in the cell or to support high-demand applications. The authors of BIB004 present a tuning mechanism that allows the network operator to dynamically adapt its network resource allocation between maximum perceived quality of all users (system efficiency) and the quality fairness among users. In BIB006 , an LTE downlink radio resource allocation method is proposed to avoid playback interruptions, where the scheduling algorithm takes into account information provided at the DASH video server, namely the bitrate of the available versions, so as to optimize the wireless resources (time, frequency and spatial domain) of the LTE system. A QoE-aware real-time adaptive resource allocation algorithm is presented in BIB014 that also takes into account the user's video required data rate. The goal is to provide a flexible tool within LTE systems to achieve a desired trade-off between fairness and efficiency among users. A QoE-oriented cross-layer optimization framework enabling efficient allocation of wireless resources in video delivery over LTE is described in BIB012 , which takes into account the packet loss rate as the relevant input for the QoE-oriented scheduler (instead of the data rate, as adopted by the works mentioned previously). Solutions that consider more than one relevant parameter have also been proposed for the QoE optimization process in video streaming services. A QoE-aware scheduling algorithm that allocates radio resources in a High-Speed Downlink Packet Access (HSDPA) network is proposed in BIB001 , where the users' MOS requirements are derived from metrics like average data rate and packet loss rate, while the mapping between these parameters and QoE scores was generated based on a trained random neural network. Considering the use of similar metrics (amount of lost packets and source rate of the video signal), the authors of BIB007 propose a MOS-driven energy efficient power allocation algorithm over OFDMA based wireless networks, which enables the design of a scheduling scheme that jointly optimizes MOS and the system power consumption. In order to improve the system QoE level while guaranteeing the fairness for users, a crosslayer resource allocation problem is modeled and formulated in BIB008 . This scheduling algorithm considers the transmission bitrate as well as the video quality fluctuation that affects the system QoE. Other passive end-user device scheduling schemes follow an approach where video QoE is assessed through metrics that need to be estimated at the network side because, although they are available on the end-user device, these metrics are not reported to the base station (otherwise the end-user device would be regarded as active, which falls under the category of the works that will be presented in Section IV-B). The authors of BIB013 propose a QoE-aware scheduling solution for HTTP progressive video downloading in OFDMA systems, where the amount of video data stored in the player buffer is estimated at the base station and adopted as the main criterion for resource allocation. The scheduler then works by reducing the resources assigned to users with enough buffered data and by transferring these to users with a low buffer level, in order to diminish the number of playback interruptions. In BIB015 , and in order to try to avoid rebuffering, a QoE-aware scheduling algorithm is presented for video delivery over wireless LTE networks, where the state of the users' buffer is also estimated at the base station from the periodic Medium Access Control (MAC) layer acknowledgments received from each user. Online scheduling policies to optimize QoE for video-on-demand applications are proposed in BIB031 , where the QoE of each flow is measured by the duration of video playback interruptions and the playback buffer status is estimated at the wireless access point. The authors studied, in particular, the heavy-traffic regime in order to address not only the long-term average performance, but also short-term QoE performance. In BIB024 , a lower bound for the rebuffering percentage (percentage of the total streaming time spent on rebuffering) is presented for the transmission of video content over cellular networks. This analytic approach is followed by a scheduling strategy based on the same QoE parameter (estimated at the base station), which is more suitable to use only in the congestion cell state as, in this conditions, it increases the average user's throughput and cell capacity with an insignificant decrease of the fairness. Another QoE-based scheduling policy that can be adopted, specially when dealing with scalable video streaming, is based on priority marking of video flows. The main goals are to avoid the need of video content processing capabilities at the base station and to minimize delay bound violations under congestion for the most important priority classes. This approach has been adopted in BIB016 for the scheduling of multiple medical video streams in wireless networks, whereas the authors of BIB025 focus on scalable video streaming over LTE systems. A similar scheduling policy is proposed in BIB026 for video delivery over IEEE 802.11n/ac networks, where a lowcomplexity QoE scheduling scheme prioritizes packets taking into account how important they are in enhancing the video quality. 2) Voice over IP: VoIP can also benefit from QoE-oriented scheduling. Considering LTE networks, the authors of BIB020 designed an algorithm to meet the tight delay requirements of VoIP and to solve, at the same time, the problem of radio resource scarcity, where the maximum delay of a client with a certain average transmitting rate is determined theoretically by a mathematical model also proposed by them. The work described in BIB021 also presents a QoE-driven LTE scheduling for VoIP application where, by taking into account the delay and the packet loss rate obtained at the scheduler itself, the resource allocation is prioritized according to the QoE requirements of the users, while enabling the maximization of the number of users per cell. In BIB009 , a QoE model for mobile VoIP services is proposed, which is based on the amount of user effort to continue a conversation. This human effort is estimated from the user's conversations (through the processing of the semantic information in the audio streams) and it is also used to formulate a QoE-oriented resource allocation scheme as a non-cooperative game between the provider and the VoIP user experiencing a deteriorating QoE. 3) Web Browsing & Instant Messaging: In addition to video delivery and VoIP, the user experience in some other wireless applications can also be enhanced by the use of QoE-aware scheduling methods. In BIB002 , the authors address web browsing applications and estimate the service response time as a function of the user data rate, so as to derive a mapping function from these QoS metrics to subjective quality satisfaction, having as final goal the incorporation of this knowledge into the radio resource allocation algorithm for OFDMA systems. A scheduling algorithm to provide fair QoE to instant messaging service over cellular networks is proposed in BIB017 , where the subservice that a user is currently focusing on is defined as the representative service and the respective QoE is based on the experienced delay; more specifically, the scheduling scheme first finds the user with the worst QoE and then performs resource allocation taking into account the respective representative service. 4) Unspecified Applications: Some authors have also proposed QoE-oriented wireless resources scheduling solutions without addressing any particular application. In order to carry out QoE-aware power allocation in multi-user Orthogonal Frequency-Division Multiplexing (OFDM) systems, a scheme that maps data rate into MOS values is described in BIB005 ; the authors claim that, even with limited amount of power, the proposed algorithm can lead to enhanced user experiences. In BIB010 , a QoE-driven resource allocation technique is presented for downlink multi-user multi-service OFDM systems. The QoE estimation models consider the required data rate of the different services in order to minimize the total consumed power with lower computational complexity when scheduling subcarriers and allocating transmission power. Also considering OFDM systems and by taking into account the required data rate of several multimedia applications, the authors of BIB018 presented a scheduling algorithm that tries to maximize the overall total QoE, while guaranteeing all the users can perceive a relatively good QoE level and an acceptable fairness in the system. In [61] , a QoE-oriented resource allocation problem is formulated among BSs as a local cooperation game, where BSs are encouraged to cooperate with their adjacent cells in user scheduling. The goal is to mitigate intercell interference in the OFDMA-based cellular networks, where the performance metric jointly considers spectrum efficiency, user fairness and service satisfaction, being the latter based on a mapping between data rate and MOS. The authors of BIB022 also addressed the inter-cell interference coordination by proposing a QoE-aware PF resource allocation strategy for multi-cell OFDMA networks, where a new relationship between data rate and MOS, followed by a new utility function, are introduced in order to achieve QoE maximization as well as fairness among users for heterogeneous service requirements. The works referred in the previous paragraph take into account only the data rate as a relevant parameter for QoE optimization. Nevertheless, other approaches have also been considered. In BIB011 , a QoE-based carrier scheduling scheme is proposed for multi-service LTE-Advanced networks; by taking into account packet delay and the data rate required by each service, the component carriers are dynamically scheduled so as to improve QoE and fairness for the users. Two QoEaware joint subcarrier and power radio resource allocation algorithms are presented in BIB019 for the downlink of an heterogeneous OFDMA system, where resource allocation is based on the QoE (delay, data rate and packet loss rate) of each heterogeneous service flow: the first algorithm tries to assure the same level of QoE to all users, whereas the second scheme performs a trade-off between the users' QoE and the spectral efficiency of the system. The authors of BIB023 propose a radio resource allocation control scheme, which aims at minimizing the global drop out rate of users in cellular networks; the impatience caused by the network (e.g., if a download time is too long) is first modeled, in order to subsequently estimate the drop out probability. In BIB027 , an application-aware resource block and power allocation for LTE is proposed, which tries to increase the overall QoE by giving priority to users with real-time applications over delay-tolerant ones. Another QoE management solution for LTE systems is proposed in BIB028 , which autonomously monitors the traffic and dynamically derives QoE targets (e.g., download time for web pages, data rate for videos) for individual application sessions based on their characteristics (content and traffic pattern). These QoE targets are then converted to per session bandwidth targets and enforced by a flexible self-programming scheduler, in order to maximize the QoE without significantly degrading the overall resource allocation and throughput efficiency. In BIB029 , a two-step radio resource allocation algorithm is presented for an LTE network, which provides a trade-off between QoE and spectral efficiency: in the first step, it allocates resources according to which application the user equipment is using, so that a minimum QoE is guaranteed for each user; in the second step, the algorithm's goal is to improve the system throughput by allocating the remaining resources to the users with the best channel conditions. The authors of BIB030 propose a fluctuationsaware scheduler, which takes into account the evolution of the achieved throughput over time (per user) and the impact of this evolution on the end-user QoE. More specifically, the scheduler tries to provide the best possible combinations of resources that maintain the past average throughput values per user, in order to avoid throughput fluctuations and, thus, providing better QoE. A summary of the aforementioned passive end-user device strategies is given in Table I . This table also enables to point out that data rate is a parameter that plays a central role when performing QoE-oriented scheduling using this type of strategies for video streaming as well as for unspecified applications. On the other hand, delay is of prime importance compared to other parameters for applications such as VoIP, web browsing, and instant messaging.