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VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions
KTH. Hive Streaming AB, Stockholm, Sweden..ORCID-id: 0000-0002-1135-8863
Maastricht Univ, Maastricht, Netherlands..
2020 (engelsk)Inngår i: 2020 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM) / [ed] Atzmuller, M Coscia, M Missaoui, R, Institute of Electrical and Electronics Engineers (IEEE) , 2020, s. 486-493Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Live video streaming has become a mainstay as a standard communication solution for several enterprises worldwide. To efficiently stream high-quality live video content to a large amount of offices, companies employ distributed video streaming solutions which rely on prior knowledge of the underlying evolving enterprise network. However, such networks are highly complex and dynamic. Hence, to optimally coordinate the live video distribution, the available network capacity between viewers has to be accurately predicted. In this paper we propose a graph representation learning technique on weighted and dynamic graphs to predict the network capacity, that is the weights of connections/links between viewers/nodes. We propose VStreamDRLS, a graph neural network architecture with a self-attention mechanism to capture the evolution of the graph structure of live video streaming events. VStreamDRLS employs the graph convolutional network (GCN) model over the duration of a live video streaming event and introduces a self-attention mechanism to evolve the GCN parameters. In doing so, our model focuses on the GCN weights that are relevant to the evolution of the graph and generate the node representation, accordingly. We evaluate our proposed approach on the link prediction task on two real-world datasets, generated by enterprise live video streaming events. The duration of each event lasted an hour. The experimental results demonstrate the effectiveness of VStreamDRLS when compared with state-of-the-art strategies. Our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/vstreamdrls.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE) , 2020. s. 486-493
Serie
Proceedings of the IEEE-ACM International Conference on Advances in Social Networks Analysis and Mining, ISSN 2473-9928
Emneord [en]
Dynamic graph representation learning, Self-attention mechanism, Video streaming
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-300221DOI: 10.1109/ASONAM49781.2020.9381430ISI: 000678816900077Scopus ID: 2-s2.0-85098823882OAI: oai:DiVA.org:kth-300221DiVA, id: diva2:1588930
Konferanse
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), DEC 07-10, 2020, ELECTR NETWORK
Merknad

QC 20230307

Tilgjengelig fra: 2021-08-30 Laget: 2021-08-30 Sist oppdatert: 2023-03-07bibliografisk kontrollert
Inngår i avhandling
1. Enabling Enterprise Live Video Streaming with Reinforcement Learning and Graph Neural Networks
Åpne denne publikasjonen i ny fane eller vindu >>Enabling Enterprise Live Video Streaming with Reinforcement Learning and Graph Neural Networks
2022 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Over the last decade, video has vastly become the most popular way the world consumes content. Due to the increased popularity, video has been a strategic tool for enterprises. More specifically, enterprises organize live video streaming events for both internal and external purposes in order to attract large audiences and disseminate important information. However, streaming a high- quality video internally in large multinational corporations, with thousands of employees spread around the world, is a challenging task. The main challenge is to prevent catastrophic network congestion in the enterprise network when thousand of employees attend a high-quality video event simultaneously. Given that large enterprises invest a significant amount of their annual budget on live video streaming events, it is essential to ensure that the office network will not be congested and each viewer will have high quality of experience during the event.

To address this challenge, large enterprises employ distributed live video streaming solutions to distribute high-quality video content between viewers of the same network. Such solutions rely on prior knowledge of the enterprise network topology to efficiently reduce the network bandwidth requirements during the event. Given that such knowledge is not always feasible to acquire, the distributed solutions must detect the network topology in real-time during the event. However, distributed solutions require a service to detect the network topology in the first minutes of the event, also known as the joining phase. Failing to promptly detect the enterprise network topology negatively impacts the event’s performance. In particular, distributed solutions may establish connections between viewers of different offices with limited network capacity. As a result, the enterprise network will be congested, and the employees will drop the event from the beginning of the event if they experience video quality issues.

In this thesis, we investigate and propose novel machine learning models allowing the enterprise network topology service to detect the topology in real- time. In particular, we investigate the network distribution of live video streaming events caused by the distributed software solutions. In doing so, we propose several graph neural network models to detect the network topology in the first minutes of the event. Live video streaming solutions can adjust the viewers’ connections to distribute high-quality video content between viewers of the same office, avoiding the risk of network congestion. We compare our models with several baselines in real-world datasets and show that our models achieve significant improvement via empirical evaluations.

Another critical factor for the efficiency of live video streaming events is the enterprise network topology service latency. Distributed live video streaming solutions require minimum latency to infer the network topology and adjust the viewers’ connections. We study the impact of the graph neural network size on the model’s online inference latency and propose several knowledge distillation strategies to generate compact models. Therefore, we create models with significantly fewer parameters, reducing the online inference latency while achieving high accuracy in the network topology detection task. Compared with state-of-the-art approaches, our proposed models have several orders of magnitude fewer parameters while maintaining high accuracy.

Furthermore, we address the continuously evolving enterprise network topology problem. Modern enterprise networks frequently change their topology to manage their business needs. Therefore, distributed live video streaming solutions must capture the network topology changes and adjust their network topology detection service in real time. To tackle this problem, we propose several novel machine learning models that exploit historical events to assist the models in detecting the network topology in the first minutes of the event. We investigate the distribution of the viewers participating in the events. We propose efficient reinforcement learning and meta-learning techniques to learn the enterprise network topology for each new event. By applying meta-learning and reinforcement learning, we can generalize network topology changes and ensure that every viewer will have a high-quality experience during an event. Compared with baseline approaches, we achieved superior performance in establishing connections between viewers of the same office in the first minutes of the event. Therefore, we ensure that distributed solutions provide a high return on investment in every live video streaming event without risking any enterprise network congestion. 

sted, utgiver, år, opplag, sider
KTH Royal Institute of Technology, 2022
Serie
TRITA-EECS-AVL ; 2022:69
Emneord
Graph Neural Networks, Reinforcement Learning, Meta-Learning, Knowledge Distillation, Enterprise Liveo Video Streaming
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-320919 (URN)978-91-8040-398-6 (ISBN)
Disputas
2022-11-21, SAL-C, Electrum building, Kistagången 16, Stockholm, 09:00 (engelsk)
Opponent
Veileder
Merknad

QC 20221102

Tilgjengelig fra: 2022-11-02 Laget: 2022-11-02 Sist oppdatert: 2022-11-18bibliografisk kontrollert

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