Online Learning for Adaptive Video Streaming in Mobile Networks

Karagkioules, Theodoros; Paschos, Georgios S.; Liakopoulos, Nikolaos; Fiandrotti, Attilio; Tsilimantos, Dimitrios; Cagnazzo, Marco

doi:10.1145/3460819

Cited by 5 publications

(4 citation statements)

References 44 publications

(14 reference statements)

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“…Later, the concept was developed for online monocular depth estimation [14]. Online learning has been shown to improve streaming policies [15]. Our work is connected to these studies as they employ video depth estimation in an online environment, similar to our objective of developing video extrapolation networks that function with online streams of video sequences.…”

Section: Related Workmentioning

confidence: 96%

All Predictions Matter: an Online Video Prediction Approach

Vijayaratnam,

Cagnazzo,

Valenzise

et al. 2023

2023 11th European Workshop on Visual Information Processing (EUVIP)

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To effectively manage and utilize the massive amount of visual data generated by the surging number of videos, decision-making systems must predict and reason about future outcomes. This paper proposes a novel online approach for video prediction that enables continual learning in the presence of new data, as periodic training of neural networks may not be practical. We utilize all predictions, including intermediate computations obtained during the inference process, to improve the performance of video prediction. To achieve this, we incorporate a weighting scheme in the loss that accounts for all the predictions during the learning process. Additionally, we leverage semantic segmentation to assess the performance of extrapolated frames by focusing on the position of the objects in the scene. Our approach stands out from state-of-the-art methods as it uses intermediate predictions, which are available due to the iterative nature of forecasting future frames. Our method improves the offline counterpart for the same network by 1.45 dB for predicting five steps in the future.

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Section: Related Workmentioning

confidence: 96%

All Predictions Matter: an Online Video Prediction Approach

Vijayaratnam,

Cagnazzo,

Valenzise

et al. 2023

2023 11th European Workshop on Visual Information Processing (EUVIP)

Self Cite

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“…Their no-regret algorithm adapts caching and routing decisions to any file request pattern. In [19] an asymptotically optimal online learning algorithm for video rate adaptation in HTTP Adaptive Streaming under no channel model assumptions is presented. The work [20] studies network power and bandwidth allocation under adversarial costs with bounded variations in consecutive slots.…”

Section: Related Work User Association (Ua)mentioning

confidence: 99%

“…Theorem 1 (PerOnE No-regret). For a Lipschitz-continuous and convex objective function V (•), with L Lipschitz constant, and M I , M J as in (19), a stepsize η and T time horizon, PerOnE achieves the regret bound:…”

Section: B Perone: Online User Association With No Regretmentioning

confidence: 99%

See 1 more Smart Citation

Blind Optimal User Association in Small-Cell Networks

Chatzieleftheriou¹,

Destounis²,

Paschos³

et al. 2021

Preprint

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We learn optimal user association policies for traffic from different locations to Access Points(APs), in the presence of unknown dynamic traffic demand. We aim at minimizing a broad family of α-fair cost functions that express various objectives in load assignment in the wireless downlink, such as total load or total delay minimization. Finding an optimal user association policy in dynamic environments is challenging because traffic demand fluctuations over time are non-stationary and difficult to characterize statistically, which obstructs the computation of costefficient associations. Assuming arbitrary traffic patterns over time, we formulate the problem of online learning of optimal user association policies using the Online Convex Optimization (OCO) framework. We introduce a periodic benchmark for OCO problems that generalizes state-of-the-art benchmarks. We exploit inherent properties of the online user association problem and propose PerOnE, a simple online learning scheme that dynamically adapts the association policy to arbitrary traffic demand variations. We compare PerOnE against our periodic benchmark and prove that it enjoys the no-regret property, with additional sublinear dependence of the network size. To the best of our knowledge, this is the first work that introduces a periodic benchmark for OCO problems and a no-regret algorithm for the online user association problem. Our theoretical findings are validated through results on a real-trace dataset.

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AP-assisted adaptive video streaming in wireless networks with high-density clients

Wu,

Yuan,

et al. 2024

Computer Communications

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Online Learning for Adaptive Video Streaming in Mobile Networks

Cited by 5 publications

References 44 publications

All Predictions Matter: an Online Video Prediction Approach

All Predictions Matter: an Online Video Prediction Approach

Blind Optimal User Association in Small-Cell Networks

AP-assisted adaptive video streaming in wireless networks with high-density clients

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