Delivering video content from a video server to viewers over the Internet is time-consuming in the streaming workflow and has to be handled to offer an uninterrupted streaming experience. The delay is particularly problematic for live streaming. Some streaming-based applications such as virtual events, online learning, webinars, and all-hands meetings require low latency for their operation. Video streaming is ubiquitous in a plethora of applications, devices, and fields. Delivering high Quality of Experience (QoE) to the streaming viewers is of crucial importance, while the requirement to process a large amount of data in order to satisfy such QoE cannot be handled with human-constrained possibilities. Artificial Intelligence (AI) and Machine Learning (ML) techniques can be leveraged to calculate expected network data rates, predict requested video contents and thus, perform content-aware encoding, predict flash crowd formation that impacts the overall network traffic, enable personalized content recommendations, understand a user’s viewing behavior, and enable more informed video caching decisions, and in several other ways. The second workshop on Design, Deployment, and Evaluation of Network-assisted Video Streaming (ViSNext) aims to bring together researchers and developers to satisfy the data-intensive processing requirements and QoE challenges of live video streaming applications through leveraging AI-based approaches. We warmly invite the submission of original, previously unpublished papers addressing key issues in this area, but not limited to:

AI-based resource allocation for live streaming
Using AI/ML techniques for optimizing Interactive Streaming and User-Generated Content
The tradeoff between QoE enhancement and network overhead: AI approaches
Using AI/ML at the network edge and the cloud for supporting video streaming
AI/ML-enabled caching of video chunks
Experience and lessons learned by deploying AI/ML algorithms for large-scale network-assisted video streaming
Design, analysis, and evaluation of AI-based Adaptive Bitrate (ABR) algorithms for video streaming
Network aspects in video streaming: cloud computing, virtualization techniques, network control, and management, including SDN, NFV, and network programmability
AI/ML-based solutions for supporting streaming applications high-speed user mobility
Analysis, modeling, and experimentation of WebRTC, Low-Latency DASH, and Low-Latency CMAF for DASH
Reproducible research in adaptive video streaming: datasets, evaluation methods, benchmarking, standardization efforts, open-source tools
AI/ML-based techniques for live streaming in 5G and 6G networks
AI/ML-based techniques for improving infotainment QoE in automotive applications.