Video Optimization through Hybrid FQ-Codel Algorithm

Video data forms the major traffic in today’s internet. Quality Of internet video has a great impact on user engagement. Video encoding and streaming over wireless networks become a big concern. Real time video service requires low end-to-end delay, which mainly consists of video encoding delay, queuing delay, and transmission delay. The major concern is to reduce the queuing delay which has high impact on the video quality. Bufferbloat is one of the main reasons for experiencing high queue latency at the intermediate nodes. Several AQM techniques were used in reducing queue latency, but optimization of buffer size is never achieved. In this work, Hybrid FQCoDel mechanism is proposed which is the combination of both CoDel and Adaptive CoDel AQM mechanism with fair queuing for the reduction of queuing delay and to optimize video transmission. Here the Adaptive CoDel is used for video traffic whereas the CoDel is used for other network flow. The fairness in the network queuing is achieved through the mechanisms of network classifier and the scheduler. Hybrid FQ-CoDeL is used for effective reduction of the queuing delay and to increase the network bandwidth utilization for video streams which will result in better quality of video.