Congestion-aware edge caching for adaptive video streaming in Information-Centric Networks
Abstract
This paper proposes a network-aware resource management scheme that improves the quality of experience (QoE) for adaptive video streaming in CDNs and Information-Centric Networks (ICN) in general, and Dynamic Adaptive Streaming over HTTP (DASH) in particular. By utilizing the DASH manifest, the network (by way of a logically centralized controller) computes the available link resources and schedules the chunk dissemination to edge caches ahead of the end-user's requests. Our approach is optimized for multi-rate DASH videos. We implemented our resource management scheme, and demonstrated that in the scenario when network conditions evolve quickly, our approach can maintain smooth high quality playback. We show on actual video server data and in our own simulation environment that a significant reduction in peak bandwidth of 20% can be achieved using our approach.
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Using videoand network-aware prefetching strategy, the delivered video quality increases by 20%, compared to a DASH system without prefetching.
“Using videoand network-aware prefetching strategy, the delivered video quality increases by 20%, compared to a DASH system without prefetching.”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [281]
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Simsek, M., Aijaz, A., Dohler, M., Sachs, J., & Fettweis, G. (2016) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2016.2525398 · Cited by 753 · see citing works
QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
Barakabitze, A. A., Barman, N., Ahmad, A., Zadtootaghaj, S., Sun, L., Martini, M. G., & Atzori, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2958784 · Cited by 239 · see citing works
Quality of Experience-Centric Management of Adaptive Video Streaming Services: Status and Challenges
Petrangeli, S., Hooft, J. V. D., Wauters, T., & Turck, F. D. (2018) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3165266 · Cited by 38 · see citing works
Video delivery networks: Challenges, solutions and future directions
Fan, Q., Yin, H., Min, G., Yang, P., Luo, Y., Lyu, Y., Huang, H., & Jiao, L. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.04.011 · Cited by 29 · see citing works
- Authors
- Yu, Y.-T., Bronzino, F., Fan, R., Westphal, C., & Gerla, M.
- Affiliations
- University of California, Los Angeles (US)
- Rutgers, The State University of New Jersey (US)
- Venue
- 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC)
- Publisher
- IEEE
- Published
- 2015-01
- Pages
- 588-596
- ISSN
- 2331-9852 (ISSN-L)
- OpenAlex
- W1536542037
- Cited by
- 33
- References
- 0
- Citations by year
- 2022: 1 · 2021: 2 · 2020: 7 · 2019: 1 · 2018: 5 · 2017: 6 · 2016: 7 · 2015: 4
- FWCI
- 5.46
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Consumer Communications and Networking Conference (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Keywords
- edge caching, adaptive video streaming, information-centric networking, DASH, content delivery networks, chunk scheduling, quality of experience
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