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Live video streaming has become a mainstay as a standard communicationsolution for several enterprises worldwide. To efficiently stream high-qualitylive video content to a large amount of offices, companies employ distributedvideo streaming solutions which rely on prior knowledge of the underlyingevolving enterprise network. However, such networks are highly complex anddynamic. Hence, to optimally coordinate the live video distribution, theavailable network capacity between viewers has to be accurately predicted. Inthis paper we propose a graph representation learning technique on weighted anddynamic graphs to predict the network capacity, that is the weights ofconnections/links between viewers/nodes. We propose VStreamDRLS, a graph neuralnetwork architecture with a self-attention mechanism to capture the evolutionof the graph structure of live video streaming events. VStreamDRLS employs thegraph convolutional network (GCN) model over the duration of a live videostreaming event and introduces a self-attention mechanism to evolve the GCNparameters. In doing so, our model focuses on the GCN weights that are relevantto the evolution of the graph and generate the node representation,accordingly. We evaluate our proposed approach on the link prediction task ontwo real-world datasets, generated by enterprise live video streaming events.The duration of each event lasted an hour. The experimental results demonstratethe effectiveness of VStreamDRLS when compared with state-of-the-artstrategies. Our evaluation datasets and implementation are publicly availableat https://github.com/stefanosantaris/vstreamdrls

VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions