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Liu Lab
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The lab explores ways to deliver video over wireless networks.
AboutAbout the lab
Optimizing video transmission over wireless networks.
The Liu Lab studies how to send video data over wireless networks.
As wireless devices become widespread, many kinds of video data are being transferred and shared over networks.
Because wireless networks are unstable and have limited available bandwidth, the lab works on resource allocation, edge computing, and error recovery for video streaming, including immersive video such as VR.
The lab uses AI techniques such as deep neural networks and deep reinforcement learning, together with optimization theory.
Key terms
- Immersive video
- Video, such as VR, that makes viewers feel as if they are inside the scene.
- Edge computing
- An approach in which computation is performed close to users' devices.
- Packet loss
- A phenomenon in which packets, the small units that data is divided into, are lost during transmission.
ResearchResearch themes
Wireless networks have unstable communication and limited available bandwidth. Under these conditions, the lab studies optimal resource allocation, error recovery, and edge computing methods for delivering video, including immersive video such as VR.
*Illustrative imagePredicting the viewing angle in immersive video
If the viewing angle (viewport) in immersive video such as VR can be predicted, the system can avoid sending the parts that will not be watched and send the parts the viewer wants to see in high quality. The lab works on methods that predict future viewing angles from past viewing logs using deep neural networks.
*Illustrative imageEfficient video transmission methods
Video transmission is one of the most important parts of a video system. Because recently established methods require modeling of the network and its resources, the lab is developing new transmission methods that combine AI techniques such as deep reinforcement learning with optimization theory.
*Illustrative imageError-resilient video transmission
Packet loss is unavoidable in wireless networks, and video is especially vulnerable to it because its frames depend on one another. The lab works on methods that deliver video effectively even over lossy networks.
MembersFaculty
The Liu Lab consists of faculty members, JSPS invited researchers from abroad, research fellows, doctoral, master's, and undergraduate students, exchange students, visiting scholars, and a secretary.As of 2026
- Associate ProfessorZhi Liu
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Wireless router, internal components (LevelOne WBR-6002)” Mk2010 / CC BY-SA 4.0 (cropped)
- Predicting the viewing angle in immersive video: “Sony-PlayStation-4-PSVR-Headset-Mk1-FL” Evan-Amos / Public domain (cropped)
- Efficient video transmission methods: “2253S H.264 video capture device” Lambtron / CC BY-SA 4.0 (cropped)
- Error-resilient video transmission: “Intel WLAN card 4965AG MM2” Sadenäyttely / CC BY-SA 4.0 (cropped)
- Related lab (Ohzahata Lab): “Cellular network antenna mount Haringey London” Acabashi / CC BY-SA 4.0 (cropped)
- Related lab (Asaeda Lab): “The ESA Seeker autonomous rover during tests at Paranal” ESA/RAL Space/ESO / CC BY 3.0 (cropped)
- Related lab (Ohama-Santoso Lab): “NASA secure communication console at National Cryptologic Museum.agr” ArnoldReinhold / CC BY-SA 4.0 (cropped)
- Related lab (Kurihara Lab): “Data storage mediums” Unnerving duck / CC0 (cropped)
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