Roger Immich

QoE-aware FEC mechanism for intrusion detection in multi-tier Wireless Multimedia Sensor Networks

Wireless Multimedia Sensor Networks (WMSNs) play an important role in pervasive and ubiquitous systems. The multimedia content in such networks has the potential of enhancing the level of information collected, enlarging the range of coverage, and enabling multi-view support. For WMSN applications, the multi-tier network architecture has proven to be more beneficial than a single-tier in terms of energy-efficiency, scalability, functionality and reliability. In this context, a multimedia intrusion detection application appears as a promising application of multi-tier WMSNs, where the lower tier can detect the intruder using scalar sensors, and the higher tier camera nodes will be woken up to send real time video sequences from the detected area. The transmission of multimedia content requires a certain quality level from the user perspective, while energy consumption and network overhead should be minimized. Among the existing mechanisms for improving video transmissions, Forward Error Correction (FEC) can be regarded as a suitable solution to improve video quality level from the user point-of-view. In this work, we propose a Quality of Experience (QoE)-aware FEC mechanism for WMSNs, which creates redundant packets based on impact of the frame on the user experience. According to the simulation results, our proposed mechanism achieved similar video quality level compared with standard FEC, while reducing the transmission of redundant packets, which will bring many benefits in a resource-constrained system.

Adaptive video-aware FEC-based mechanism with unequal error protection scheme

Real-time video services over wireless networks are becoming a part of everyday life and have been used to spread information ranging from education to entertainment content. However, the challenge of dealing with the fluctuating bandwidth, scarce resources and the time-varying error rate of theses networks, unveils the need for an error-resilient video transport. In this context, Forward Error Correction (FEC) approaches are required to provide the distribution of video applications for wireless users with Quality of Experience (QoE) assurance. This work proposes an adaptive cross-layer Video-Aware FEC mechanism with Unequal Error Protection (UEP) scheme to enhance video transmission in wireless networks, while increasing the user satisfaction and improving the usage of wireless resources. The benefit and impact of the proposed mechanism are demonstrated by using simulation and assessed through objective and subjective QoE metrics.

A quality of experience handover system for heterogeneous multimedia wireless networks

The convergence of emerging real-time multimedia services, the increasing coverage of wireless networks and the ever-growing popularity of mobile devices, are leading to an era of user-centric multimedia wireless services. In this scenario, heterogeneous communications will co-exist and ensure that the end-user is always best connected. However, the Quality of Experience (QoE) support for emerging video applications in multi-operator environments remains a significant challenge and is crucial for the success of wireless multimedia systems. This paper presents a Quality of Experience Handover Architecture for Converged Heterogeneous Wireless Networks, called QoEHand. QoEHand allows users of multimedia content to be always best connected in IEEE 802.11e and IEEE 802.16e environments. Simulation results show the impact and benefit of the proposed solution in multi-access and multi-operator wireless scenarios by using objective and subjective QoE metrics.

Cross-Layer FEC-Based mechanism for packet loss resilient video transmission

Real-time video transmission over wireless networks is now a part of the daily life of users, since it is the vehicle that delivers a wide range of information. The challenge of dealing with the fluctuating bandwidth, scarce resources and time-varying error levels of these networks, reveals the need for packet-loss resilient video transport. Given these conditions, Forward Error Correction (FEC) approaches are desired to ensure the delivery of video services for wireless users with Quality of Experience (QoE) assurance. This work proposes a Cross-layer Video-Aware FEC-based mechanism with Unequal Error Protection (UEP) scheme for packet loss resilient video transmission in wireless networks, which can increase user satisfaction and improve the use of resources. The advantages and disadvantages of the developed mechanism are highlighted through simulations and assessed by means of both subjective and objective QoE metrics.

Adaptive QoE-driven video transmission over Vehicular Ad-hoc Networks

Vehicular Ad-hoc Networks (VANETs) are envisioned to offer support for a large variety of distributed applications that range from alerting drivers to autonomous driving features and video services. The use of video-equipped vehicles, with support for live transmission, unveils the need for an adaptive Quality of Experience (QoE)-driven mechanism to overcome several challenges and provide a good video quality. These challenges can range from the scarce network resources and vehicles movement to the time-varying channel conditions and high error rates. Adaptive Forward Error Correction (FEC) schemes can be tailored to shield the video transmission with QoE assurance. The adaptive QoE-driven and FEC-based mechanism proposed in the paper safeguard real-time video transmissions over high-mobility and error-prone networks, improving both the usage of resources and the user experience. Benefits and footprint are evidenced through experiments and QoE assessments. The results demonstrate that the proposed mechanism is able to outperform both non-adaptive and adaptive competitors.

QoE-driven video delivery improvement using packet loss prediction

The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes.

Ensuring QoE in wireless networks with adaptive FEC and Fuzzy Logic-based mechanisms

Online video transmissions over wireless networks are rising in popularity and have already become part of our daily life. In the meantime, it is necessary to address a number of challenges ranging from the scarce resources, time-varying, and high error rates, to the fluctuating bandwidth, unveiling the need for an adaptive mechanism to ensure a good video transmission. Adaptive Forward Error Correction (FEC) techniques with Quality of Experience (QoE) assurance are appropriate to deliver QoE-aware video data to wireless users in dynamic and high error rates networks. This paper proposes an adaptive Video-aware FEC and Fuzzy Logic-based mechanism to shield realtime video transmissions against packet loss in wireless networks, improving both user experience and the usage of resources. The benefits and drawbacks of the proposed mechanism compared with exiting work are demonstrated through simulations and assessed with QoE metrics.

Towards the enhancement of UAV video transmission with motion intensity awareness

The use of video-equipped Unmanned Aerial Vehicles (UAV) has been increasing recently, along with the number of available applications for military and civilian employment. This unveils the need for an adaptive video-aware mechanism capable of overcoming a number of challenges related to the scarce network resources, device movement, as well as high error rates, to ensure a good video quality delivery. Forward Error Correction (FEC) techniques can be tailored to provide adaptive protection with Quality of Experience (QoE) assurance over error-prone and high-mobility networks. Besides that, unique characteristics of each video sequence, such as the spatial complexity and the temporal intensity, strongly affect how the QoE will be impacted by the packet loss. This paper proposes an adaptive motion intensity and video-aware FEC mechanism with the aid of Fuzzy logic to safeguard UAV real-time video transmissions against packet loss, providing a better user experience, while saving resources. The advantages and drawbacks of the proposed mechanism in comparison to the related work are evidenced through experiments and assessed by using QoE metrics.

A mobile QoE Architecture for Heterogeneous Multimedia Wireless Networks

One of the main requirements in this emerging wireless multimedia era is the Quality of Experience (QoE) assurance for 2D or 3D video applications in heterogeneous multi-operator environments. Therefore, this paper proposes a QoE Architecture for Heterogeneous Multimedia Wireless Networks, called QoEHand. QoEHand extends the Media Independent Handover (MIH)/IEEE 802.21 proposal with QoE-awareness, seamless mobility, dynamic class of service mapping and a set of content adaptation schemes. The proposed solution allows the best connection and considers the QoE needs of mobile clients and available wireless resources in IEEE 802.11e and IEEE 802.16e service classes. Simulation experiments were carried out to show the impact and benefits of QoEHand on the users perception, by using objective and subjective QoE metrics.

Towards a QoE-driven mechanism for improved H.265 video delivery

The employment of video-equipped vehicles is growing apace. Following the same trend is the number of available applications and services place at disposal in Vehicular Ad-hoc Networks (VANETs). Conversely, video services are commonly referred as one of the most stringent applications, on the grounds that it requires a quality-aware steady and uninterrupted flow of information. Because of that, a number of challenges arose, including how to deal with the scarce network resources, the high-error rates and the time-varying channel conditions. This unveils the need for an adaptive video-aware and Quality of Experience (QoE)-driven mechanism to take care of these challenges and deliver video sequences with good quality. To this end, Forward Error Correction (FEC) techniques can be customized to support video transmissions with QoE assurance over high-mobility and error-prone networks. The adaptive QoE-driven mechanism proposed in this paper improves the resilience of real-time video transmissions against packet losses. It relies on the combination of VANETs characteristics and High Efficiency Video Coding (HEVC) details to provide a tailored amount of redundancy, which improves both the usage of resources and the user experience. The advantages and footprint of the mechanism are evidenced through extensive experiments and QoE assessments, proving that the proposed mechanism outperforms non-adaptive and also adaptive competitors.