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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  <identifier identifierType="DOI">10.5281/zenodo.3761854</identifier>
  <creators>
    <creator>
      <creatorName>Panagiotis Athanasoulis</creatorName>
      <affiliation>Singular Logic S.A., Athens, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Emmanouil Christakis</creatorName>
      <affiliation>Visual Computing Lab (VCL), Information Technologies Institute (ITI), Centre for Research and Technology - Hellas (CERTH), Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Konstantinos Konstantoudakis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5092-8796</nameIdentifier>
      <affiliation>Visual Computing Lab (VCL), Information Technologies Institute (ITI), Centre for Research and Technology - Hellas (CERTH), Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Petros Drakoulis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3434-3290</nameIdentifier>
      <affiliation>Visual Computing Lab (VCL), Information Technologies Institute (ITI), Centre for Research and Technology - Hellas (CERTH), Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Stamatia Rizou</creatorName>
      <affiliation>Singular Logic S.A., Athens, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Avi Weit</creatorName>
      <affiliation>IBM Research, Haifa, Israel</affiliation>
    </creator>
    <creator>
      <creatorName>Alexandros Doumanoglou</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4337-1720</nameIdentifier>
      <affiliation>Visual Computing Lab (VCL), Information Technologies Institute (ITI), Centre for Research and Technology - Hellas (CERTH), Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Nikolaos Zioulis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7898-9344</nameIdentifier>
      <affiliation>Visual Computing Lab (VCL), Information Technologies Institute (ITI), Centre for Research and Technology - Hellas (CERTH), Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Dimitrios Zarpalas</creatorName>
      <affiliation>Visual Computing Lab (VCL), Information Technologies Institute (ITI), Centre for Research and Technology - Hellas (CERTH), Thessaloniki, Greece</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Optimizing QoE and Cost in a 3D Immersive Media Platform: A Reinforcement Learning Approach</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>immersive media</subject>
    <subject>cognitive network optimizer</subject>
    <subject>reinforcement learning</subject>
    <subject>5g</subject>
    <subject>5g media</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-04-22</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3761854</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3761853</relatedIdentifier>
  </relatedIdentifiers>
  <version>1</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/761699/">761699</awardNumber>
      <awardTitle>Programmable edge-to-cloud virtualization fabric for the 5G Media industry</awardTitle>
    </fundingReference>
  </fundingReferences>
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