QoE-driven video delivery improvement using packet loss prediction

Abstract

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.

Keywords::

• forward error correction (FEC)
• video-aware FEC
• motion vectors (MVs)
• QoE
• neural networks
• ant colony optimization
• unequal error protection (UEP)
• packet loss rate prediction

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was funded by the Brazilian National Counsel of Technological and Scientific Development (CNPq) and also supported by the Intelligent Computing in the Internet of Services (iCIS) project [CENTRO-07-ST24-FEDER-002003], co-financed by QREN, Mais Centro Program.