Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery From Multiple Servers
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1132
- Type
- D - Journal article
- DOI
-
10.1109/tmm.2015.2425228
- Title of journal
- IEEE Transactions on Multimedia
- Article number
- -
- First page
- 893
- Volume
- 17
- Issue
- 6
- ISSN
- 1520-9210
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
C - Communications and Networking (Comms)
- Citation count
- 25
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work, published in IEEE-TMM, the flagship multimedia journal, is the result of international collaboration between Essex, EPFL-Switzerland and UCLA-USA. Significantly it was the first to use advanced reinforcement learning to decide optimal coding and scheduling policies for PRLC coded data from multiple servers considering hard delivery deadlines. Importantly this work offers continuous playback and guarantees small quality variations; both increasingly important for users/network operators/content providers. The work is generic enabling wide application and was cited by leading researchers (Fitzek/TU-Dresden). An invited talk was given in COST Action IC1104 workshop (https://network-coding.eu/novisad/). Concepts first described here are extended in subsequent papers.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -