Bruno Zatt

Leonardo Gobatto, Mateus Saquetti, Claudio Diniz et al.

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.

Arthur Cerveira, Luciano Agostini, Bruno Zatt et al.

This paper presents a memory assessment of the next-generation Versatile Video Coding (VVC). The memory analyses are performed adopting as a baseline the state-of-the-art High-Efficiency Video Coding (HEVC). The goal is to offer insights and observations of how critical the memory requirements of VVC are aggravated, compared to HEVC. The adopted methodology consists of two sets of experiments: (1) an overall memory profiling and (2) an inter-prediction specific memory analysis. The results obtained in the memory profiling show that VVC access up to 13.4x more memory than HEVC. Moreover, the inter-prediction module remains (as in HEVC) the most resource-intensive operation in the encoder: 60%-90% of the memory requirements. The inter-prediction specific analysis demonstrates that VVC requires up to 5.3x more memory accesses than HEVC. Furthermore, our analysis indicates that up to 23% of such growth is due to VVC novel-CU sizes (larger than 64x64).