Deep neural network–based enhancement for image and video streaming systems: A survey and future directions

R Lee, SI Venieris, ND Lane - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual
apps spanning from on-demand movies and 360° videos to video-conferencing and live …

A Survey on Intelligent Solutions for Increased Video Delivery Quality in Cloud-Edge-End Networks

W Shi, Q Li, Q Yu, F Wang, G Shen… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
The digital age has brought a significant increase in video traffic. This traffic growth, driven
by rapid internet advancements and a surge in multimedia applications, presents both …

Learning in situ: a randomized experiment in video streaming

FY Yan, H Ayers, C Zhu, S Fouladi, J Hong… - … USENIX Symposium on …, 2020 - usenix.org
We describe the results of a randomized controlled trial of video-streaming algorithms for
bitrate selection and network prediction. Over the last year, we have streamed 38.6 years of …

Xlink: Qoe-driven multi-path quic transport in large-scale video services

Z Zheng, Y Ma, Y Liu, F Yang, Z Li, Y Zhang… - Proceedings of the …, 2021 - dl.acm.org
We report XLINK, a multi-path QUIC video transport solution with experiments in Taobao
short videos. XLINK is designed to meet two operational challenges at the same time:(1) …

Neural-enhanced live streaming: Improving live video ingest via online learning

J Kim, Y Jung, H Yeo, J Ye, D Han - … of the Annual conference of the …, 2020 - dl.acm.org
Live video accounts for a significant volume of today's Internet video. Despite a large
number of efforts to enhance user quality of experience (QoE) both at the ingest and …

Cloud-device collaborative learning for multimodal large language models

G Wang, J Liu, C Li, Y Zhang, J Ma… - Proceedings of the …, 2024 - openaccess.thecvf.com
The burgeoning field of Multimodal Large Language Models (MLLMs) has exhibited
remarkable performance in diverse tasks such as captioning commonsense reasoning and …

OnRL: Improving mobile video telephony via online reinforcement learning

H Zhang, A Zhou, J Lu, R Ma, Y Hu, C Li… - Proceedings of the 26th …, 2020 - dl.acm.org
Machine learning models, particularly reinforcement learning (RL), have demonstrated great
potential in optimizing video streaming applications. However, the state-of-the-art solutions …

Tambur: Efficient loss recovery for videoconferencing via streaming codes

M Rudow, FY Yan, A Kumar… - … USENIX Symposium on …, 2023 - usenix.org
Packet loss degrades the quality of experience (QoE) of videoconferencing. The standard
approach to recovering lost packets for long-distance communication where retransmission …

Nemo: enabling neural-enhanced video streaming on commodity mobile devices

H Yeo, CJ Chong, Y Jung, J Ye, D Han - Proceedings of the 26th Annual …, 2020 - dl.acm.org
The demand for mobile video streaming has experienced tremendous growth over the last
decade. However, existing methods of video delivery fall short of delivering high-quality …

Achieving consistent low latency for wireless real-time communications with the shortest control loop

Z Meng, Y Guo, C Sun, B Wang, J Sherry… - Proceedings of the …, 2022 - dl.acm.org
Real-time communication (RTC) applications like video conferencing or cloud gaming
require consistent low latency to provide a seamless interactive experience. However …