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- Antaris, StefanosHive Streaming AB, Sweden (author)
Meta-reinforcement learning via buffering graph signatures for live video streaming events
- Article/chapterEnglish2021
Publisher, publication year, extent ...
- 2022-01-19
- New York, NY, USA :Association for Computing Machinery (ACM),2021
- printrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:kth-316104
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-316104URI
- https://doi.org/10.1145/3487351.3490973DOI
Supplementary language notes
- Language:English
- Summary in:English
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- SwePubSwePub
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Notes
- Part of ISBN 9781450391283QC 20220822
- In this study, we present a meta-learning model to adapt the predictions of the network's capacity between viewers who participate in a live video streaming event. We propose the MELANIE model, where an event is formulated as a Markov Decision Process, performing meta-learning on reinforcement learning tasks. By considering a new event as a task, we design an actor-critic learning scheme to compute the optimal policy on estimating the viewers' high-bandwidth connections. To ensure fast adaptation to new connections or changes among viewers during an event, we implement a prioritized replay memory buffer based on the Kullback-Leibler divergence of the reward/throughput of the viewers' connections. Moreover, we adopt a model-agnostic meta-learning framework to generate a global model from past events. As viewers scarcely participate in several events, the challenge resides on how to account for the low structural similarity of different events. To combat this issue, we design a graph signature buffer to calculate the structural similarities of several streaming events and adjust the training of the global model accordingly. We evaluate the proposed model on the link weight prediction task on three real-world datasets of live video streaming events. Our experiments demonstrate the effectiveness of our proposed model, with an average relative gain of 25% against state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/melanie
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Programvaruteknik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Software Engineering hsv//eng
- graph signatures
- meta-reinforcement learning
- video streaming
- Cell proliferation
- Computer vision
- Markov processes
- Reinforcement learning
- Global models
- Graph signature
- Live video streaming
- Markov Decision Processes
- Meta-learning models
- Metalearning
- Network Capacity
- Structural similarity
- Video-streaming
Added entries (persons, corporate bodies, meetings, titles ...)
- Rafailidis, DimitriosUniversity of Thessaly, Greece (author)
- Girdzijauskas, SarunasKTH,Programvaruteknik och datorsystem, SCS(Swepub:kth)u1k70r02 (author)
- Hive Streaming AB, SwedenUniversity of Thessaly, Greece (creator_code:org_t)
Related titles
- In:Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021New York, NY, USA : Association for Computing Machinery (ACM), s. 385-392
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