Meta-Reinforcement Learning via Buffering Graph Signatures for Live
Video Streaming Events
- URL: http://arxiv.org/abs/2111.09412v1
- Date: Sun, 3 Oct 2021 14:03:22 GMT
- Title: Meta-Reinforcement Learning via Buffering Graph Signatures for Live
Video Streaming Events
- Authors: Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
- Abstract summary: 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 evaluate the proposed model on the link weight prediction task on three real-world of live video streaming events.
- Score: 4.332367445046418
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
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