T
Ting Bi
Researcher at Dublin City University
Publications - 25
Citations - 353
Ting Bi is an academic researcher from Dublin City University. The author has contributed to research in topics: Heterogeneous network & Wireless network. The author has an hindex of 9, co-authored 23 publications receiving 189 citations.
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Journal ArticleDOI
A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities
TL;DR: The state-of-the-art on adaptive 360° video delivery solutions considering end-to-end video streaming in general and then specifically of 360°video delivery are presented.
Journal ArticleDOI
Perceived Synchronization of Mulsemedia Services
TL;DR: The results of a subjective study carried out to explore the temporal boundaries within which haptic and air-flow media objects can be successfully synchronized with video media show that skews between sensorial media and multimedia might still give the effect that the mulsemedia sequence is "in-sync" and provide certain constraints under which synchronization errors might be tolerated.
Journal ArticleDOI
DBNS: A Distributed Blockchain-Enabled Network Slicing Framework for 5G Networks
Mohammed Amine Togou,Ting Bi,Kapal Dev,Kevin McDonnell,Aleksandar Milenovic,Hitesh Tewari,Gabriel-Miro Muntean +6 more
TL;DR: A distributed blockchain-enabled network slicing framework that enables service and resource providers to dynamically lease resources to ensure high performance for their end-to-end services and improve users' experience with diverse services and reduce providers' capital and operational expenditures is introduced.
Proceedings ArticleDOI
A DASH-based Mulsemedia Adaptive Delivery Solution
TL;DR: Results of real life subjective testing indicate that the average levels of user satisfaction when exposed to content with the mulsemedia effects are better or at least equal to those when no such effects were included.
Proceedings ArticleDOI
Reputation-based network selection solution for improved video delivery quality in heterogeneous wireless network environments
TL;DR: A user location-aware reputation-based network selection solution which aims at improving the video delivery in a heterogeneous wireless network environment by selecting the best value network and shows that the proposed solution improves the video Delivery quality in comparison with the case when a classic network selection mechanism was employed.