RISE: Multi-Link Proactive Low-Latency Video Streaming for Teleoperation in Fading Channels

Throughput-critical applications, such as teleoperation, require robust and low-latency communication systems to ensure seamless performance. In this work, we propose the opensource predictive multi-link teleoperation video stream system for fading channels called Reliable Intelligent Stream Encoding system (RISE). It features a proactive bitrate control based on predicted channel conditions together with a modified version of the SEAMLESS multi-link protocol. Additionally, video stream queue feedback is evaluated to dynamically adjust the bitrate if predictions are too optimistic. Thereby, video transmission delays are minimized, preventing video frame drops due to an unacceptable age-of-information. We validate the performance of RISE in a laboratory setup using realistic radio channel emulation. The results demonstrate significant latency and video frame delivery improvements compared to conventional singlelink and constant bitrate approaches. By leveraging SEAMLESS in addition to using the predicted bitrate with queue feedback approach, the median data rate of the video stream was doubled. Furthermore, RISE prevents frame drops in all investigated channel configurations while minimizing the queue delay. Thus, the proposed approach demonstrates the potential for improvement in the overall reliability of real-world teleoperation systems.

Authors

Institutions

Publication Details

Published
2025-06-17
DOI
https://doi.org/10.1109/vtc2025-spring65109.2025.11174790
Citations
2
Primary Topic
Image and Video Quality Assessment
Type
article
Field-Weighted Citation Impact
2.55
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

RISE: Multi-Link Proactive Low-Latency Video Streaming for Teleoperation in Fading Channels

Hendrik Schippers, Tim Gebauer, Christian Wietfeld, Karsten Heimann
2 citations
Image and Video Quality Assessment
2.55
article

RISE: Multi-Link Proactive Low-Latency Video Streaming for Teleoperation in Fading Channels

Hendrik Schippers, Tim Gebauer, Christian Wietfeld, Karsten Heimann
article en
2 citations

Abstract

Throughput-critical applications, such as teleoperation, require robust and low-latency communication systems to ensure seamless performance. In this work, we propose the opensource predictive multi-link teleoperation video stream system for fading channels called Reliable Intelligent Stream Encoding system (RISE). It features a proactive bitrate control based on predicted channel conditions together with a modified version of the SEAMLESS multi-link protocol. Additionally, video stream queue feedback is evaluated to dynamically adjust the bitrate if predictions are too optimistic. Thereby, video transmission delays are minimized, preventing video frame drops due to an unacceptable age-of-information. We validate the performance of RISE in a laboratory setup using realistic radio channel emulation. The results demonstrate significant latency and video frame delivery improvements compared to conventional singlelink and constant bitrate approaches. By leveraging SEAMLESS in addition to using the predicted bitrate with queue feedback approach, the median data rate of the video stream was doubled. Furthermore, RISE prevents frame drops in all investigated channel configurations while minimizing the queue delay. Thus, the proposed approach demonstrates the potential for improvement in the overall reliability of real-world teleoperation systems.

TU Dortmund University (DE)
Openalex Percentile: Top 9%
Image and Video Quality Assessment
2.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.