Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events

TL;DR

This paper introduces MELANIE, a meta-learning model that uses graph signatures and prioritized replay buffers to adaptively predict network capacity during live video streaming events, improving accuracy and responsiveness.

Contribution

The paper proposes a novel meta-learning framework with graph signature buffers and prioritized replay for real-time network capacity prediction in live streaming.

Findings

Achieved an average 25% relative gain over state-of-the-art methods.

Effectively adapts to low structural similarity between different streaming events.

Demonstrated robustness across three real-world datasets.

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