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MMSys'21 - Multi-access edge computing for adaptive bitrate video streaming

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All rights reserved. ©2020
All rights reserved. ©2020
Multi-access Edge Computing for
Adaptive Bitrate Video Streaming
1
ACM MMSys’21 Doctoral Symposium
September 30, 2021
Jesús Aguilar Armijo
Christian Doppler laboratory ATHENA | Alpen-Adria-Universität Klagenfurt | Austria
jesus.aguilar@aau.at | athena.itec.aau.at
All rights reserved. ©2020
● Introduction
● Research Questions
● Methodology
● Ongoing And Future Work
● Q&A
Table of
content
All rights reserved. ©2020
2
Introduction
All rights reserved. ©2020
3
All rights reserved. ©2020
● Video streaming traffic is the most important service in mobile
networks, with increasing traffic over the years*, therefore improve
the content delivery and creating a high quality of experience (QoE)
becomes essential
● Multi-Access Edge Computing (MEC) is an emerging paradigm
that brings computational power and storage closer to the user. It
reduces latency, ensures highly efficient network operation
and improves the user experience
● Video streaming can leverage MEC advantages to improve
content delivery:
○ User awareness and radio information available to perform
better adaptation decisions
○ Storage capabilities for caching
○ Computing power to deploy mechanisms that assist video
streaming process
Video Streaming And MEC
All rights reserved. ©2020
*Ericsson. 2019. Ericsson Mobility Report. Technical Report.
4
Research questions
All rights reserved. ©2020
5
All rights reserved. ©2020
1. How can network assistance for HTTP Adaptive Streaming (HAS) be realized by MEC
functionality?
We can create and deploy new mechanisms that leverage MEC storage and computing power to guide
the video streaming process and improve the users’ QoE
2. How to use Radio Access Network (RAN) and HAS client context information and perform
aggregate analytics in the edge?
At the edge, we have radio network information and each user’s requirements. This information can
coordinate the video streaming process among all the clients improving the QoE and fairness.
3. How can we perform user behavior analysis and predictions to support low latency?
The historical radio and user information available at the edge can be used to predict future content
requests and future network conditions using machine learning techniques.
4. How can collaboration between different edge computing nodes assist the video streaming
process?
The caching process should not be limited to users within the same edge node. We can expand the
caching scope to more users using the 4G and 5G communication between base stations and edge
nodes.
Research Questions
All rights reserved. ©2020
6

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MMSys'21 - Multi-access edge computing for adaptive bitrate video streaming

  • 1. All rights reserved. ©2020 All rights reserved. ©2020 Multi-access Edge Computing for Adaptive Bitrate Video Streaming 1 ACM MMSys’21 Doctoral Symposium September 30, 2021 Jesús Aguilar Armijo Christian Doppler laboratory ATHENA | Alpen-Adria-Universität Klagenfurt | Austria jesus.aguilar@aau.at | athena.itec.aau.at
  • 2. All rights reserved. ©2020 ● Introduction ● Research Questions ● Methodology ● Ongoing And Future Work ● Q&A Table of content All rights reserved. ©2020 2
  • 4. All rights reserved. ©2020 ● Video streaming traffic is the most important service in mobile networks, with increasing traffic over the years*, therefore improve the content delivery and creating a high quality of experience (QoE) becomes essential ● Multi-Access Edge Computing (MEC) is an emerging paradigm that brings computational power and storage closer to the user. It reduces latency, ensures highly efficient network operation and improves the user experience ● Video streaming can leverage MEC advantages to improve content delivery: ○ User awareness and radio information available to perform better adaptation decisions ○ Storage capabilities for caching ○ Computing power to deploy mechanisms that assist video streaming process Video Streaming And MEC All rights reserved. ©2020 *Ericsson. 2019. Ericsson Mobility Report. Technical Report. 4
  • 5. Research questions All rights reserved. ©2020 5
  • 6. All rights reserved. ©2020 1. How can network assistance for HTTP Adaptive Streaming (HAS) be realized by MEC functionality? We can create and deploy new mechanisms that leverage MEC storage and computing power to guide the video streaming process and improve the users’ QoE 2. How to use Radio Access Network (RAN) and HAS client context information and perform aggregate analytics in the edge? At the edge, we have radio network information and each user’s requirements. This information can coordinate the video streaming process among all the clients improving the QoE and fairness. 3. How can we perform user behavior analysis and predictions to support low latency? The historical radio and user information available at the edge can be used to predict future content requests and future network conditions using machine learning techniques. 4. How can collaboration between different edge computing nodes assist the video streaming process? The caching process should not be limited to users within the same edge node. We can expand the caching scope to more users using the 4G and 5G communication between base stations and edge nodes. Research Questions All rights reserved. ©2020 6
  • 8. All rights reserved. ©2020 ● The research methodology will follow and repeat the traditional “design - implement - analyze” work cycles: 1. Develop concepts, algorithms and protocols 2. Implement prototype software 3. Do quantitative analyses Methodology All rights reserved. ©2020 ● What do we consider improving video streaming services? ○ Improve Quality of Experience (QoE) ○ Improve fairness among the users ○ Increase bandwidth savings ○ Reduce latency ○ Enable higher qualities (4K, 8K...) and new services (Augmented/Virtual reality, 360º video...) ○ Reduce storage needed at different points of the network 8
  • 9. Ongoing and future work All rights reserved. ©2020 9
  • 10. All rights reserved. ©2020 ● CMAF (Common Media Application Format) is not yet supported by most devices but provides great benefits: ○ Less storage needed ○ Bandwidth savings ○ More cache efficiency ● Our approach ○ Use CMAF in the backhaul ○ Perform transmuxing into the requested format at the edge ● Three procedures for the paper: ○ Analytical model: Up to 20% bandwidth savings for different media format distributions ○ Processing time: When the edge node has 1.64 or more compute power per segment than the server, the performance of the proposed model is the same or better. ○ Real experiments: Extra added latency in a real world-like setting ● The paper was published in the IEEE ISM’20 conference Dynamic Segment Repackaging at the Edge for HTTP Adaptive Streaming All rights reserved. ©2020 10
  • 11. All rights reserved. ©2020 ● Edge-based scheme that supports the client-based ABR algorithm, improving its adaptation decisions ● Provide awareness of other users requests, segment prefetching support and different level of subscription ● Operates in an on-the-fly manner, minimum latency is added ● The 𝛼 value in our algorithm can prioritize QoE or fairness, as our preferences ● Results show that EADAS improves the final QoE score by 4.6%, 23.5%, and 24.4% for the BBA, TBA, and SARA ABR algorithms, respectively. Even in high fairness scenarios, EADAS improves fairness by 11%, 3.4% and 5.8% for BBA, TBA, and SARA, respectively. ● This paper was published at the 46𝑡ℎ Conference on Local Computer Networks (LCN) 2021 EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Video Streaming All rights reserved. ©2020 1
  • 12. All rights reserved. ©2020 ● We developed a simulator named ANGELA: HTTP Adaptive Streaming and Edge Computing Simulator. It supports (1) realistic radio layer simulation and multimedia content, (2) flexible implementation of edge computing mechanisms, (3) access to radio and player metrics at the edge, and (4) a wide variety of metrics to evaluate the video streaming session performance ● Deploy an edge-based mechanism that uses machine learning techniques to predict future content segment requests and future network conditions to support low latency streaming ● Inter edge computing node collaboration: Establish communication between different nearby edge nodes for aggregate caching and content serving Ongoing And Future Work All rights reserved. ©2020 12
  • 13. Thank you Q&A All rights reserved. ©2020 13