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.

Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment

“…The Subjective Pseudo Quality Assessment model or PSQA (Pseudo Subjective Quality Assessment) is an example of nonintrusive method (NR). This model uses an RNN (Random Neural Network) [31][32][33] to learn and recognize the relationship between video and the characteristics of the network with the quality perceived by users.…”

Towards Improved Vehicular Information-Centric Networks by Efficient Caching Discovery

“…The Vehicular Named Data Networking (VNDN) technology advent promisingly paves the way to tackle challenges of high performing large-scale content distribution provisioning in Internet Protocol (IP)-centric Ad-Hoc Vehicular Networks (VANETs) [1]. By definition, the IP paradigm cannot both offer users massive content delivery, as well as ensure the requirements of distributed VANET application concerning Quality of Service (QoS) and Quality of Experience (QoE) [2][3][4]. The reason behind the aforementioned IP issues in VANETS arises from the intense vehicle mobility patterns, which result in constant topological dynamics.…”