Quality Of Experience Support Research Articles

Dynamic Microservice Allocation for Virtual Reality Distribution With QoE Support

Virtual Reality (VR) content is gaining popularity and allowing users to immerse themselves in a new world over the Internet. However, the high-demand for resources and the low latency requirements of VR services require changes in the current 5G networks to deliver VR with quality assurance. Microservices present a suitable model for deploying services at different levels of a 5G fog computing architecture for managing traffic and providing Quality of Experience (QoE) guarantees to VR clients. However, finding the most suitable fog node to allocate microservices for VR clients in QoE-aware 5G scenarios is a difficult task. This article proposes a QoE VR-based mechanism for allocating microservice dynamically in 5G architectures, called Fog4VR. Fog4VR determines the optimal fog node to allocate the VR microservice based on delay, migration time, and resource utilization rate. This article also presents the INFORMER, an integer linear programming model aiming to find the optimal global solution for microservice allocation. Results obtained with INFORMER serve as a baseline to evaluate Fog4VR in different scenarios using a simulation environment. Results demonstrate the efficiency of Fog4VR compared to existing mechanisms in terms of cost, migration time, fairness index, and QoE.

A multi-tier fog content orchestrator mechanism with quality of experience support

Video-on-Demand (VoD) services create a demand for content orchestrator mechanisms to support Quality of Experience (QoE). Fog computing brings benefits for enhancing the QoE for VoD services by caching the content closer to the user in a multi-tier fog architecture, considering their available resources to improve QoE. In this context, it is mandatory to consider network, fog node, and user metrics to choose an appropriate fog node to distribute videos with QoE support properly. In this article, we introduce a content orchestrator mechanism, called of Fog4Video, which chooses an appropriate fog node to download video content. The mechanism considers the available bandwidth, delay, and cost, besides the QoE metrics for VoD, namely number of stalls and stalls duration, to deploy VoD services in the opportune fog node. Decision-making acknowledges periodical reports of QoE from the clients to assess the video streaming from each fog node. These values serve as inputs for a real-time Analytic Hierarchy Process method to compute the influence factor for each parameter and compute the QoE improvement potential of the fog node. Fog4Video is executed in fog nodes organized in multiple tiers, having different characteristics to provide VoD services. Simulation results demonstrate that Fog4Video transmits adapted videos with 30% higher QoE and reduced monetary cost up to 24% than other content request mechanisms.

A QoE-Oriented Scheduling Scheme for Energy-Efficient Computation Offloading in UAV Cloud System

Air ground integrated mobile cloud computing (MCC) provides UAVs with more flexibility and resilience from the cloud computing architecture. However, the increasing aerial mobile data requires heterogeneous quality of experience (QoE) for aerial accessing network. In addition, for the persistent flying, energy efficiency during the computation offloading should also be under consideration. This paper proposes an energy-efficient resource allocation scheme with the ability of QoE enhancement. Various aerial offloading data with different QoE requirements is stored and relayed in the multi-queueing architecture. Hence offloading rate differentiation is utilized to ensure the high-priority data a better QoE. The satisfaction function is designed with respect to energy efficiency and actual performance experienced by UAV. By using the Lyapunov optimization technique, the problem can be decoupled into two independent sub-problems. The first one is rate control associated with multi-queueing architecture in ground base-station (GBS) that manages the aerial offloading data from the UAVs according to the queue state information. The second one is resource allocation associated with the strategy of subcarrier assignment and power allocation according to the channel state information. The experiments demonstrate the algorithm has great properties such as maximization of the UAVs' satisfaction, the reliable heterogeneous QoE support and enhancement of the UAVs' transmission energy efficiency.

Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency.

Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to set up a Flying Ad Hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapid deployable systems. The user experience on watching videos transmitted over FANETs should always be satisfactory even under influence of topology changes caused by the energy consumption of UAVs. In addition, the FANET must keep the UAVs cooperating as much as possible during a mission. However, one of the main challenges in FANET is how to mitigate the impact of limited energy resources of UAVs on the FANET operation in order to monitor the environment for a long period of time. In this sense, UAV replacement is required in order to avoid the premature death of nodes, network disconnections, route failures, void areas, and low-quality video transmissions. In addition, decision-making must take into account energy consumption associated with UAV movements, since they are generally quite energy-intensive. This article proposes a cooperative UAV scheme for enhancing video transmission and global energy efficiency called VOEI. The main goal of VOEI is to maintain the video with QoE support while supporting the nodes with a good connectivity quality level and flying for a long period of time. Based on an Software Defined Network (SDN) paradigm, the VOEI assumes the existence of a centrailized controller node to compute reliable and energy-efficiency routes, as well as detects the appropriate moment for UAV replacement by considering global FANET context information to provide energy-efficiency operations. Based on simulation results, we conclude that VOEI can effectively mitigate the energy challenges of FANET, since it provides energy-efficiency operations, avoiding network death, route failure, and void area, as well as network partitioning compared to state-of-the-art algorithm. In addition, VOEI delivers videos with suitable Quality of Experience (QoE) to end-users at any time, which is not achieved by the state-of-the-art algorithm.

A Game Theory Approach for Platoon-Based Driving for Multimedia Transmission in VANETs

Vehicular Ad Hoc Networks (VANETs) allow users, services, and vehicles to share information and will change our life experience with new autonomous driving applications. Multimedia will be one of the core services in VANETs and are becoming a reality in smart environments, ranging from safety and security traffic warnings to live entertainment and advertisement videos. However, VANETs have a dynamic network topology with short contact time, which leads to communication flaws and delays, increasing packet loss, and decreasing the Quality of Experience (QoE) of transmitted videos. To cope with this, neighbor vehicles moving on the same direction and wishing to cooperate should form a platoon, where platoon members act as a relay node to forward video packets in autonomous VANETs. In this article, we introduce a game theory approach for platoon-based driving (GT4P) for video dissemination services in urban and highway VANET scenarios. GT4P encourages the cooperation between neighbor vehicles by offering reward (e.g., money or coupon) for vehicles participating in the platoon. In this sense, GT4P establishes a platoon by taking into account vehicle direction, speed, distance, link quality, and travel path, which reduces the impact of vehicle mobility on the video transmission. Simulation results confirm the efficiency of GT4P for ensuring video transmissions with high QoE support compared to existing platoon-based driving protocols.

Quality of experience enhancement of high efficiency video coding video streaming in wireless packet networks using multiple description coding

Recently, video streaming has attracted much attention and interest due to its capability to process and transmit large data. We propose a quality of experience (QoE) model relying on high efficiency video coding (HEVC) encoder adaptation scheme, in turn based on the multiple description coding (MDC) for video streaming. The main contributions of the paper are (1) a performance evaluation of the new and emerging video coding standard HEVC/H.265, which is based on the variation of quantization parameter (QP) values depending on different video contents to deduce their influence on the sequence to be transmitted, (2) QoE support multimedia applications in wireless networks are investigated, so we inspect the packet loss impact on the QoE of transmitted video sequences, (3) HEVC encoder parameter adaptation scheme based on MDC is modeled with the encoder parameter and objective QoE model. A comparative study revealed that the proposed MDC approach is effective for improving the transmission with a peak signal-to-noise ratio (PSNR) gain of about 2 to 3 dB. Results show that a good choice of QP value can compensate for transmission channel effects and improve received video quality, although HEVC/H.265 is also sensitive to packet loss. The obtained results show the efficiency of our proposed method in terms of PSNR and mean-opinion-score.

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends.

QoE-driven dissemination of real-time videos over vehicular networks

Live video dissemination over Vehicular Ad Hoc Networks (VANETs) is fundamental for several services, e.g., roadside video emergency, advertisement’s broadcast, and smart video surveillance. All these applications face many challenges due to stringent video quality level requirements, dynamic topologies, and broadcast environments. To cope with these challenges, as well as reduce network routing overhead, geographic Statistical Routing Protocols (SRPs) have been proposed as a suitable solution for the distribution of video flows in VANETs, usually by using positioning and Quality of Service (QoS) parameters. However, rather than only these parameters, a satisfactory video dissemination from the user’s perspective also requires video and human-awareness issues. In real situations, due to different requirements and hierarchical structures of multimedia applications, network level and position parameters alone are not enough to select the best relay nodes and build up reliable backbones to multi-hop video dissemination with satisfactory reachability and Quality of Experience (QoE) levels. This article focuses on improving the disseminated quality of on-road live videos in VANETs. Thus, we propose the cross-layer QOe-driven REceiver-based (QORE) mechanism, which is modularly coupled to SRPs to offer QoE-aware and video-related parameters for the relay node selection and backbone maintenance. Thus, nodes decide for themselves to retransmit further the video sequences, enhancing the capacity of the system in delivering videos with better QoE assurance. On top of this, an application-level Error-Control (EC) scheme, namely Interleaving, allows mitigating the effects of frame loss by spreading out the bursty losses. We added QORE to a straightforward SRP built using the Distance method, named DQORE protocol. Results show the gains of DQORE compared to SRPs, achieving video dissemination with QoE support, less routing overhead, and satisfactory reachability, in V2V topologies.

A Decision-Tree-Based Perceptual Video Quality Prediction Model and Its Application in FEC for Wireless Multimedia Communications

With the exponential growth of video traffic over wireless networked and embedded devices, mechanisms are needed to predict and control perceptual video quality to meet the quality of experience (QoE) requirements in an energy-efficient way. This paper proposes an energy-efficient QoE support framework for wireless video communications. It consists of two components: 1) a perceptual video quality model that allows the prediction of video quality in real-time and with low complexity, and 2) an application layer energy-efficient and content-aware forward error correction (FEC) scheme for preventing quality degradation caused by network packet losses. The perceptual video quality model characterizes factors related to video content as well as distortion caused by compression and transmission. Prediction of perceptual quality is achieved through a decision tree using a set of observable features from the compressed bitstream and the network. The proposed model can achieve prediction accuracy of 88.9% and 90.5% on two distinct testing sets. Based on the proposed quality model, a novel FEC scheme is introduced to protect video packets from losses during transmission. Given a user-defined perceptual quality requirement, the FEC scheme adjusts the level of protection for different components in a video stream to minimize network overhead. Simulation results show that the proposed FEC scheme can enhance the perceptual quality of videos. Compared to conventional FEC methods for video communications, the proposed FEC scheme can reduce network overhead by 41% on average.

A beaconless Opportunistic Routing based on a cross-layer approach for efficient video dissemination in mobile multimedia IoT applications

Mobile multimedia networks are enlarging the Internet of Things (IoT) portfolio with a huge number of multimedia services for different applications. Those services run on dynamic topologies due to device mobility or failures and wireless channel impairments, such as mobile robots or Unmanned Aerial Vehicle (UAV) environments for rescue or surveillance missions. In those scenarios, beaconless Opportunistic Routing (OR) allows increasing the robustness of systems for supporting routing decisions in a completely distributed manner. Moreover, the addition of a cross-layer scheme enhances the benefits of a beaconless OR, and also enables multimedia dissemination with Quality of Experience (QoE) support. However, existing beaconless OR approaches do not support a reliable and efficient cross-layer scheme to enable effective multimedia transmission under topology changes, increasing the packet loss rate, and thus reducing the video quality level based on the user’s experience. This article proposes a Link quality and Geographical beaconless OR protocol for efficient video dissemination for mobile multimedia IoT, called LinGO. This protocol relies on a beaconless OR approach and uses multiple metrics for routing decisions, including link quality, geographic location, and energy. A QoE/video-aware optimisation scheme allows increasing the packet delivery rate in presence of links errors, by adding redundant video packets based on the frame importance from the human’s point-of-view. Simulation results show that LinGO delivers live video flows with QoE support and robustness in mobile and dynamic topologies, as needed in future IoT environments.