Optimization of Quality of Experience for Video Traffic

TL;DR

This paper proposes a novel cross-layer architecture to enhance the Quality of Experience for video traffic, demonstrating superior performance over traditional non-adaptive and adaptive methods through extensive simulations.

Contribution

The paper introduces a new cross-layer architecture for optimizing video QoE and compares it against existing architectures, showing improved performance in key metrics.

Findings

Proposed architecture outperforms non-adaptive and adaptive architectures.

Significant improvements in mean opinion score, delay, and packet drop ratio.

Enhanced network utilization and reduced jitter.

Abstract

The rapid shift toward video on-demand and real time information systems has affected mobile as well as wired networks. The research community has placed a strong focus on optimizing the Quality of Experience (QoE) of video traffic, mainly because video is popular among Internet users. Techniques have been proposed in different directions towards improvement of the perception of video users. This paper investigates the performance of a novel cross-layer architecture for optimizing the QoE of video traffic. The proposed architecture is compared to two other architectures; non-adaptive and adaptive. For the former, video traffic is sent without adaptation, whereas for the later video sources adapt their transmission rate. Both are compared in terms of the mean opinion score of video sessions, number of sessions, delay, packet drop ratio, jitter and utilization. The results from extensive simulations show that the proposed architecture outperforms the non-adaptive and adaptive architectures for video traffic.