AntMind: Enhancing error protection for video streaming in wireless networks

Abstract

On-line video services are becoming a large part of the daily routines of people all over the world, where most of the content is accessed through wireless networks. Therefore, it is of ever growing importance that the negative aspects of these types of error prone networks are lessened in order to ensure adequate quality of the delivered video streams. Forward Error Correction (FEC) techniques allow the stream to be protected with an amount of redundancy to preserve the video quality during transmission. Nevertheless, some FEC schemes do not make an efficient usage of the available network resources due to unnecessary use of redundancy as a result of video-unawareness. The adaptive FEC mechanism proposed in this paper uses the motion intensity characteristics of the video and the network loss state to deliver the video streaming with adequate Quality of Experience (QoE), while keeping the use of network resources to a minimum level. It does so from a combined use of a Random Neural Network (RNN) for motion intensity classification and an Ant Colony Optimization (ACO) scheme for dynamic redundancy allocation. QoE metrics are used to assess the performance of the mechanism showing its advantages over adaptive and nonadaptive protection schemes.