Learning Social Relationship From Videos via Pre-Trained Multimodal Transformer

Teng, Yiyang; Song, Chenguang; Wu, Bin

doi:10.1109/lsp.2022.3181849

Cited by 12 publications

(1 citation statement)

References 29 publications

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“…The effective estimation for spread contributions of nodes can implement accurate spread control such as video push with a high success rate, video caching with the balance of supply and demand, and precise video-request dispatching. Numerous researchers focus on social-based video spread [27][28][29][30][31][32]. For instance, Niu et al propose a multiple-sourcedriven asynchronous information diffusion model based on substantial video diffusion traces, which makes use of the measurement results of contributions of multiple potential sources to promote video-spreading scale [31]; Wu et al propose an effective pricing-based multicast video distribution based on grid clustering, which offloads and alleviates the base station traffic load caused by the rapidly growing video demand [32].…”

Section: Introductionmentioning

confidence: 99%

A Novel Video-Spreading Strategy Based on the Joint Estimation of Social Influence and Sharing Capacity in Wireless Networks

Jia

Liang

2023

Electronics

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In this paper, we propose a novel video-sharing strategy based on the joint estimation of social influence and sharing capacity in wireless networks (SSISC), which promotes the scale and efficiency of video spread and ensures the balance of supply and demand. SSISC designs an estimation model of video-sharing gains by investigating social influence levels, sharing capacities (including capacities of information dispatching and video delivery), and predicted expansion scale. Some social parameters (e.g., centrality of degree and betweenness, and average shortest distance) and some parameters of sharing performance (e.g., number of forwarded messages and cached videos, the average time of transmission and freeze) are used to evaluate social influence, capacities of information dispatching, and video delivery; video interest levels, social relationship levels, and historical push success rates are used to predict video proliferation scale. A video-spreading strategy based on the assistance of spread nodes is designed, which controls the process of video push according to available bandwidth and push priority to balance supply and demand and ensure user experience quality. Extensive tests show how SSISC achieves much better performance results in comparison with other state-of-the-art solutions.

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