QoE-Constrained Concurrent Request Optimization Through Collaboration of Edge Servers

A quantitative relationship between Application Performance Metrics and Quality of Experience for Over-The-Top video

The rapid increase of using cloud computing encourages service vendors to supply services with different features and provide them in a service pool. Service composition (SC) problem in cloud computing environment becomes a key issue because of the increase of service quantity and user requirements of the quality of service experience. To satisfy the demands on quality of service experience and realize an efficient algorithm for SC problem, a quality of experience (QoE) evaluation model based on fuzzy analytic hierarchy process (FAHP) for SC problem is put forward first. Then, an improved artificial bee colony (IABC) optimization algorithm for QoE based SC problem is proposed. The algorithm improves the updating mechanism of scout bees by introducing current global optimal solution to accelerate convergence velocity and eventually to improve the solution quality. Finally, the experimental results on QWS dataset show that IABC has a better performance on QoE based SC problem, compared with original ABC, PSO and DE.

As media streaming grows, service providers have to focus more on users’ quality of experience (QoE). Among the metrics that influence QoE, network-oriented and media-oriented factors have been the primary concern of most existing work. Apart from the two factors, however, another crucial aspect of QoE is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the user</i>. Though the analysis and modeling of users is a turning point in the transition from QoS to QoE, the impact of users’ memory and cognitive-psychological effects have not been fully explored. In this work, we analyze, validate and quantify the relationship between multiple cognitive effects and the QoE of users in media streaming. We propose PsyQoE, a new QoE assessment framework that uniquely focuses on user perceptions during media sessions. Besides providing real-time QoE assessments through leveraging machine learning, PsyQoE also predicts the overall QoE by taking into account user biases and long-term effects. Additionally, we have designed a further modified version to cater to the needs of hardware and energy-restricted scenarios. When compared with other existing methods, our approach demonstrates enhanced performance by boosting accuracy by 7% to 16%. It also provides better interpretability and broader applicability.

Live streaming services have gained extreme popularity in recent years. Due to the spiky traffic patterns of live videos, utilizing distributed edge servers to improve viewers' quality of experience (QoE) has become a common practice nowadays. Nevertheless, the current client-driven content caching mechanism does not support pre-caching from the cloud to the edge, resulting in a considerable amount of cache misses in live video delivery. By jointly considering the features of live videos and edge servers, we propose PLVER, a proactive live video push scheme to address the cache miss problem in live video delivery. Specifically, PLVER first conducts a one-to-multiple stable allocation between edge clusters and user groups to balance the load of live traffic over the edge servers. It then adopts proactive video replication algorithms to speed up video replication among the edge servers. We conduct extensive trace-driven evaluation, covering 0.3 million Twitch viewers and more than 300 Twitch channels. The results demonstrate that with PLVER, edge servers can carry 28 and 82 percent more traffic than the auction-based replication (ABR) method and the caching on requested time (CORT) method, respectively.

Quality of Experience (QoE) is a human centric quality evaluation method which provides the blue print of human needs, perceptions, feelings and experiences with respect to a multimedia service. In a communications ecosystem, human interaction takes place alongside technological, contextual, and business domains, thus producing a holistic view on QoE formation. In this paper, we investigate the relationship between human perceived QoE and context for burgeoning 3-dimensional (3D) audio teleconferencing services. 3D audio teleconferencing applications are customizable by generating different virtual acoustic environments (VAE), where parameters such as virtual room size and competing talker conditions can be adjusted for a particular application. The impact of different VAE characteristics on perceived QoE, however, is still unknown. In this study, four QoE factors were investigated across different VAE scenarios. It was found that a) medium-size virtual rooms produce optimal perceived QoE, b) competing talkers of mixed gender could be easily located in the virtual space, and c) competing speaker gender had no significant effect on perceived audio quality

Nowadays, short video feed has attracted billions of mobile users all around the world to interact with content effortlessly, yielding an explosive growth of short video commerce. Typically, users watch full-screen short videos of a few seconds one-by-one in a watch-list generated by recommender systems, skipping those they are not interested in. However, the recommender system at the cloud makes a user-interest-specific decision mostly based on the users' behavior data collected within the application itself (e.g., users' view history), without examining the lower-layer network and communication statistics. When the playback choked due to the limited network bandwidth, the user will probably skip the video, leading to a waste of bandwidth and degradation of the user's quality of experience (QoE). Meanwhile, the excessive number of user requests to video contents raises a heavy computational load and communication cost for the recommender system at the cloud to determine which videos to be recommended and delivered to each user in a real-time manner. The advance of edge computing provides a promising avenue of deploying edge nodes with caches (e.g., household devices) beyond cloud and edge servers, such that the recommender system in the cloud can place popular video contents closer to client users, and meanwhile the contents are delivered to client users with good network condition. In this paper, we propose CFP, a cross-layer recommender system for short video streaming with fine-grained preloading technique at the network edge. CFP jointly optimizes the recommendation effect of the video application and the content preloading efficiency under various network conditions at the network edge. CFP takes a two-stage approach: the cloud server first seeks to perform edge-wise instead of user-interest-specific recommendation with neural collaborative filtering recommender, preloading a list of candidate videos to edge nodes, and each edge node, deploying the GRU with attention, then delivers the proper video contents to the client user device according to the user's preference. Trace-driven emulations demonstrate the efficiency of the proposed CFP scheme.

Edge computing, which locates edge servers with limited computing and storage resources at the edge of networks, is expected as a novel architecture for low-latency applications. In edge computing, users offload a task to edge servers due to poor computing resources and batteries of mobile devices so that the edge servers execute the offloaded task and return the result of it to users. Users can thus enjoy various applications without depending on the limitations of mobile devices. However, when the edge server’s load is too heavy, a large number of tasks will be offloaded to distant cloud servers. The long distance between users and cloud servers significantly degrades the quality of mobile applications. To prevent this problem, we propose flow splitting and aggregation schemes to improve offload delay to cloud servers in edge computing. This scheme splits TCP connections between users and cloud servers at the edge server, and then aggregates TCP connections between the edge server and cloud servers. We show the effectiveness of our scheme through simulation evaluations.

QoE-aware budgeted edge data caching online: A primal–dual approach

Edge data caching has attracted tremendous attention in recent years. Service providers can consider caching data on nearby locations to provide service for their app users with relatively low latency. The key to enhance the user experience is appropriately choose to cache data on the suitable edge servers to achieve the service providers' objective, e.g., minimizing data retrieval latency and minimizing data caching cost, etc. However, Quality of Experience (QoE), which impacts service providers' caching benefit significantly, has not been adequately considered in existing studies of edge data caching. This is not a trivial issue because QoE and Quality-of-Service (QoS) are not correlated linearly. It significantly complicates the formulation of cost-effective edge data caching strategies under the caching budget, limiting the number of cache spaces to hire on edge servers. We consider this problem of QoE-aware edge data caching in this paper, intending to optimize users' overall QoE under the caching budget. We first build the optimization model and prove the NP-completeness about this problem. We propose a heuristic approach and prove its approximation ratio theoretically to solve the problem of large-scale scenarios efficiently. We have done extensive experiments to demonstrate that the MPSG algorithm we propose outperforms state-of-the-art approaches by at least 68.77%.

Quality of Experience (QoE) is referred as level of user's satisfaction, enjoyment, learning and evaluation about the services or products. Recently quality of experience is used for improvement in product development life cycle after getting feedback from end users and service providers also use QoE to measure quality of services (QoS) of their services. Cloud computing provides services such as storage, web hosting, operating system environment, application development platform and CPU resources pay per use. End users are accessing cloud services via mobile apps, but enormous amount of temporary/cache data is generated by apps, so internal storage of mobile devices are filled quickly. Mobile device without any space in internal storage has huge impact on the performance when accessing the cloud services, which degrade the QoE of end users for particular cloud app and services. This paper presents the results of experiments conducted using two mobile devices HTC and Samsung to analyze the impact on end user's QoE during accessing cloud, when internal storage of HTC mobile device is filled and Samsung having 10 GB free space. Finally, on the basis of experimental results future changes in cloud apps are suggested for service provider to improve end user's QoE.

As an important data communication technology, the purpose of EDGE (Enhanced Data Rates for Global Evolution) network is to provide rich multimedia 3G communication services by directly enhancing the rate of exist network, before 3G network completes the commercialization. Combined with the user experience-oriented characteristic of EDGE network operation, this paper first analyses the important factors of EDGE QoE(Quality of Experience) architecture, and puts forward the concept of “Busy Time Throughput” as an important KPI (Key Performance Indicator)that impact the value of EDGE network QoE. As the period which is difficult for Service Providers (SPs) to control customers' Quality of Experience (QoE), Busy Time effective detection and analysis is an import premise to achieve the overall EDGE network QoE promotion. Then, Information Theory model is put forward to detect the Busy Time dynamically. At the end of this paper, typical EDGE data network is taken as an example to analyze this model.

Video viewing is currently the primary form of entertainment for modern people due to the rapid development of mobile devices and 5G networks. The combination of pervasive edge devices and adaptive bitrate streaming technologies can lessen the effects of network changes, boosting user quality of experience (QoE). Even while edge servers can offer near-end services to local users, it is challenging to accommodate a high number of mobile users in a dynamic environment due to their restricted capacity to maximize user long-term QoE. We are motivated to integrate user allocation and bitrate adaptation into one optimization objective and propose a multiagent reinforcement learning method combined with an attention mechanism to solve the problem of multiedge servers cooperatively serving users. Through comparative experiments, we demonstrate the superiority of our proposed solution in various network configurations. To tackle the edge user allocation problem, we proposed a method called attention-based multiagent reinforcement learning (AMARL), which optimized the problem in two directions, i.e., maximizing the QoE of users and minimizing the number of leased edge servers. The performance of AMARL is proved by experiments.

The outstanding increase in video traffic, puts increasing pressure on network transmission. Since the Dynamic Adaptive Streaming over HTTP (DASH) adjusts the delivery to the dynamic network conditions, it has emerged as a popular approach for video transmissions. However, bitrate switching and video rebuffering may still occur and influence negatively quality of experience (QoE). Additionally the popular videos are transmitted multiple times, which leads to high bandwidth consumption, despite large transmission redundancy. In this context, we propose a Cooperative Learning-based scheme for the smart Edge servers with cAching and Prefetching (CoLEAP) to improve the QoE of adaptive video streaming. CoLEAP employs edge servers which cache the most beneficial contents to reduce redundant video transmissions and prefetches content to decrease network transmission delay. Considering user-related information and the state of network, CoLEAP intelligently makes the most advantageous decisions of caching and prefetching by employing a novel QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we test the proposed solution in comprehensive simulated scenarios and against four alternative solutions. When compared with the existing schemes, CoLEAP increases average bitrate by up to 181.8%, reduces video rebuffering by up to 70.8% as well as decreases response time by up to 28.0%. These values result in minimum improvements of 57.4% and 29.0%, respectively in terms of cache hit rate and QoE.

As many applications and services are moving towards a more human-centered design, app vendors are taking the quality of experience (QoE) increasingly seriously. End-to-end latency is a key factor that determines the QoE experienced by users, especially for latency-sensitive applications such as online gaming, health care, critical warning systems and so on. Recently, edge computing has emerged as a promising solution to the high latency problem. In an edge computing environment, edge servers are deployed at cellular base stations, offering processing power and low network latency to users within their geographic proximity. In this paper, we tackle the user allocation problem in edge computing from an app vendor's perspective, where the vendor needs to decide which edge servers to serve which users in a specific area. Also, the vendor must consider the various levels of quality of service (QoS) for its users. Each QoS level results in a different QoE level; thus, the app vendor needs to decide the QoS level for each user so that the overall user experience is maximized. To tackle the NP-hardness of this problem, we formulate it as a potential game then propose QoEGame, an effective and efficient game-theoretic approach that admits a Nash equilibrium as a solution to the user allocation problem. Being a distributed algorithm, QoEGame is able to fully utilize the distributed nature of edge computing. Finally, we theoretically and empirically evaluate the performance of QoEGame, which is illustrated to be significantly better than the state of the art and other baseline approaches.

Virtual reality (VR) has been envisioned as the killer-application in the 5G mobile networks. Among numerous VR services, 360° video streaming is the most promising one. Nevertheless, its wide adoption is hindered by large latency incurred in cloud-based video delivery and insufficient bandwidth resource in Radio Access Network (RAN). Fortunately, the emergence of Multi-access Edge Computing (MEC) become an enabler to fulfill the potential of VR by providing caching and computing resources at network edges. Also, since a user can view only a part of the entire 360° video frame due to the limitation of eye vision, users' Quality of Experience (QoE) can be further enhanced if we can predict his Field of View (FoV). In this paper, we propose a novel MEC-assisted FoV-aware and QoE-driven Adaptive Streaming (MFQAS) scheme for 360° videos. Specifically, we first provide a comprehensive QoE model for 360° video streaming. Second, we adopt AutoRegression Moving Average (ARMA) model in FoV prediction. Finally, we propose a heuristic algorithm to optimize the caching and computing decision at MEC server based on predicted FoV so that users' QoE can be enhanced. The simulation results show that our proposed method can provide much better prediction accuracy and QoE compared with baseline algorithms.