Mechanisms for Resilient Video Transmission

TL;DR

This paper presents adaptive, cross-layer error-resilient video transmission mechanisms for wireless networks, combining FEC and UEP with various optimization techniques to improve perceived video quality and network efficiency.

Contribution

It introduces novel FEC-based, UEP mechanisms using heuristic, neural, ant colony, and fuzzy logic methods for adaptive error protection in wireless video transmission.

Findings

Proposed mechanisms outperform competitors in perceived video quality.

Adaptive redundancy strategies improve network footprint and QoE.

Realistic simulations validate the effectiveness of the approaches.

Abstract

Wireless networks are envisaged to be one of the most important technologies to provide cost-efficient content delivery, including for video applications. They will allow thousands of thousands of fixed and mobile users to access, produce, share, and consume video content in a ubiquitous way. Real-time video services over these 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 time-varying error rate of these networks, highlights the need for error-resilient video transmission. In this context, the combination of Forward Error Correction (FEC) and Unequal Error Protection (UEP) approaches is known to provide the distribution of video applications for wireless users with Quality of Experience (QoE) assurance. This thesis proposed a procedure to assess the video characteristics and their related impact on the perceived quality to end-users. This thesis proposes a series of cross-layer video-aware and FEC-based mechanisms with UEP to enhance video transmission in several types of wireless networks. A number of methods to set an adaptive amount of redundancy were used in these mechanisms, such as heuristic techniques, random neural networks, ant colony optimisation, and fuzzy logic. In the first one, heuristic techniques, the mechanisms rely on human experience to define the best strategy. The advantages and drawbacks of the proposed mechanisms were demonstrated in realistic simulations using real video sequences and actual network traces. The assessments were conducted with well-known QoE metrics. The results show that all the proposed mechanisms were able to outperform the competitors on both perceived video quality and network footprint.