Conference paper Open Access

Optimizing QoE and Cost in a 3D Immersive Media Platform: A Reinforcement Learning Approach

Panagiotis Athanasoulis; Emmanouil Christakis; Konstantinos Konstantoudakis; Petros Drakoulis; Stamatia Rizou; Avi Weit; Alexandros Doumanoglou; Nikolaos Zioulis; Dimitrios Zarpalas


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        <foaf:name>Nikolaos Zioulis</foaf:name>
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    <dct:title>Optimizing QoE and Cost in a 3D Immersive Media Platform: A Reinforcement Learning Approach</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dcat:keyword>immersive media</dcat:keyword>
    <dcat:keyword>cognitive network optimizer</dcat:keyword>
    <dcat:keyword>reinforcement learning</dcat:keyword>
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    <dct:description>&lt;p&gt;Recent advances in media-related technologies, including&amp;nbsp;capturing and processing, have facilitated novel forms&amp;nbsp;of 3D media content, increasing the degree of user immersion.&amp;nbsp;In order to ensure these technologies can readily support the&amp;nbsp;rising demand for more captivating entertainment, both the&amp;nbsp;production and delivery mechanisms should be transformed to&amp;nbsp;support the application of media or network-related optimizations&amp;nbsp;and refinements on-the-fly. Network peculiarities deriving&amp;nbsp;from geographic and other factors make it difficult for a greedy&amp;nbsp;or a supervised machine learning algorithm to successfully foresee&amp;nbsp;the need for reconfiguration of the content production or delivery&amp;nbsp;procedures. For these reasons, Reinforcement Learning (RL)&amp;nbsp;approaches have lately gained popularity as partial information&amp;nbsp;on the environment is enough for an algorithm to begin its&amp;nbsp;training and converge to an optimal policy. The contribution&amp;nbsp;of this work is a Cognitive Network Optimizer (CNO) in the&amp;nbsp;form of an RL agent, designed to perform corrective actions on&amp;nbsp;both the production and consumption ends of an immersive 3D&amp;nbsp;media platform, depending on a collection of real-time monitoring&amp;nbsp;parameters, including infrastructure, application-level and quality&amp;nbsp;of experience (QoE) metrics. Our work demonstrates CNO&amp;nbsp;approaches with different foci, i.e., a greedy maximization of&amp;nbsp;the users&amp;rsquo; QoE, a QoE-focused RL approach and a combined&amp;nbsp;QoE-and-Cost RL approach.&lt;/p&gt;</dct:description>
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