VideoTrain: A Generative Adversarial Framework for Synthetic Video Traffic Generation

Kattadige, Chamara; Muramudalige, Shashika R.; Choi, Kwon Nung; Jourjon, Guillaume; Wang, Haonan; Jayasumana, Anura P.; Thilakarathna, Kanchana

doi:10.1109/wowmom51794.2021.00034

Cited by 7 publications

(1 citation statement)

References 24 publications

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“…future bandwidth condition. GAN-based solution [104] actual video traces synthesizing video streaming data, with a focus on 360°/normal video classification. Video compression masked image modeling transformer [53] video deep video compression.…”

Section: Methodsmentioning

confidence: 99%

A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming

Zhou,

Wang,

Liu

et al. 2024

Preprint

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This paper offers an insightful examination of how currently top-trending AI technologies, i.e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology, including video generation, understanding, and streaming. It highlights the innovative use of these technologies in producing highly realistic videos, a significant leap in bridging the gap between real-world dynamics and digital creation. The study also delves into the advanced capabilities of LLMs in video understanding, demonstrating their effectiveness in extracting meaningful information from visual content, thereby enhancing our interaction with videos. In the realm of video streaming, the paper discusses how LLMs contribute to more efficient and user-centric streaming experiences, adapting content delivery to individual viewer preferences. This comprehensive review navigates through the current achievements, ongoing challenges, and future possibilities of applying Generative AI and LLMs to video-related tasks, underscoring the immense potential these technologies hold for advancing the field of video technology related to multimedia, networking, and AI communities.Impact Statement-This paper contributes to the field of video technology by examining the integration of Generative AI and Large Language Models (LLMs) in video generation, understanding, and streaming. Its exploration of these technologies offers a foundational understanding of their potential and limitations in enhancing the realism and interactivity of video content. The exploration of LLMs in video comprehension sets the stage for advancements in accessibility and interaction, promising enhanced educational tools, improved user interfaces, and advanced video analytics applications. Additionally, the paper underscores the role of LLMs in optimizing video streaming services, leading to more personalized and bandwidth-efficient platforms. This could substantially benefit the entertainment sector with adaptive streaming solutions tailored to individual preferences. By identifying key challenges and future research directions, the paper guides ongoing efforts to merge AI with video technology, while raising awareness about potential ethical issues. Its influence extends beyond academia, encouraging responsible AI development and policy-making in video technology, balancing technological advancements with ethical considerations.

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Section: Methodsmentioning

confidence: 99%