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A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. Hive Streaming AB, Stockholm, Sweden..ORCID iD: 0000-0002-1135-8863
Univ Thessaly, Volos, Greece..
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
2021 (English)In: 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) / [ed] Chen, Y Ludwig, H Tu, Y Fayyad, U Zhu, X Hu, X Byna, S Liu, X Zhang, J Pan, S Papalexakis, V Wang, J Cuzzocrea, A Ordonez, C, Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 1787-1796Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present a deep graph reinforcement learning model to predict and improve the user experience during a live video streaming event, orchestrated by an agent/tracker. We first formulate the user experience prediction problem as a classification task, accounting for the fact that most of the viewers at the beginning of an event have poor quality of experience due to low-bandwidth connections and limited interactions with the tracker. In our model we consider different factors that influence the quality of user experience and train the proposed model on diverse state-action transitions when viewers interact with the tracker. In addition, provided that past events have various user experience characteristics we follow a gradient boosting strategy to compute a global model that learns from different events. Our experiments with three real-world datasets of live video streaming events demonstrate the superiority of the proposed model against several baseline strategies. Moreover, as the majority of the viewers at the beginning of an event has poor experience, we show that our model can significantly increase the number of viewers with high quality experience by at least 75% over the first streaming minutes. Our evaluation datasets and implementation are publicly available at https://publicresearch.z13.web.core.windows.net

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. p. 1787-1796
Series
IEEE International Conference on Big Data, ISSN 2639-1589
Keywords [en]
User experience, live video streaming, graph reinforcement learning
National Category
Learning Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-315414DOI: 10.1109/BigData52589.2021.9671949ISI: 000800559501107Scopus ID: 2-s2.0-85125360036OAI: oai:DiVA.org:kth-315414DiVA, id: diva2:1681652
Conference
9th IEEE International Conference on Big Data (IEEE BigData), 15-18 December, 2021, Virtual
Note

Part of proceedings: ISBN 978-1-6654-3902-2

QC 20220707

Available from: 2022-07-07 Created: 2022-07-07 Last updated: 2022-11-02Bibliographically approved
In thesis
1. Enabling Enterprise Live Video Streaming with Reinforcement Learning and Graph Neural Networks
Open this publication in new window or tab >>Enabling Enterprise Live Video Streaming with Reinforcement Learning and Graph Neural Networks
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
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. 

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2022
Series
TRITA-EECS-AVL ; 2022:69
Keywords
Graph Neural Networks, Reinforcement Learning, Meta-Learning, Knowledge Distillation, Enterprise Liveo Video Streaming
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-320919 (URN)978-91-8040-398-6 (ISBN)
Public defence
2022-11-21, SAL-C, Electrum building, Kistagången 16, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 20221102

Available from: 2022-11-02 Created: 2022-11-02 Last updated: 2022-11-18Bibliographically approved

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Antaris, StefanosGirdzijauskas, Sarunas

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