Improving Content-Aware Video Streaming in Congested Networks with In-Network Computing

TL;DR

This paper proposes an in-network computing approach with a packet drop algorithm and hardware module to enhance content-aware video streaming in congested networks, significantly reducing packet loss with minimal resource impact.

Contribution

It introduces a novel in-network computing method that monitors packet content and prioritizes video packets, improving streaming resilience under network congestion.

Findings

Reduces intra-predicted packet loss by over 80%

Achieves this with negligible resource and performance costs

Enhances video streaming quality in congested networks

Abstract

Network congestion and packet loss pose an ever-increasing challenge to video streaming. Despite the research efforts toward making video encoding schemes resilient to lossy network conditions, forwarding devices have not considered monitoring packet content to prioritize packets and minimize the impact of packet loss on video transmission. In this work, we advocate in favor of in-network computing employing a packet drop algorithm and an in-network hardware module to devise a solution for improving content-aware video streaming in congested network. Results show that our approach can reduce intra-predicted packet loss by over 80% at negligible resource usage and performance costs.