An empirical study of bandwidth predictability in mobile computing
Proceedings of the third ACM international workshop on Wireless network testbeds, experimental evaluation and characterization
Fine-grained scalable streaming from coarse-grained videos
Proceedings of the 18th international workshop on Network and operating systems support for digital audio and video
Subjective impression of variations in layer encoded videos
IWQoS'03 Proceedings of the 11th international conference on Quality of service
Dynamic adaptive streaming over HTTP --: standards and design principles
MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems
Mobile video streaming using location-based network prediction and transparent handover
Proceedings of the 21st international workshop on Network and operating systems support for digital audio and video
Network characteristics of video streaming traffic
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
Video streaming using a location-based bandwidth-lookup service for bitrate planning
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Wireless/mobile video streaming has become increasingly popular, which makes wireless link bandwidth scarce. To provide streaming services to mobile users, it is crucial to adapt to the link condition and traffic fluctuation. We investigate which factors in natural environments and user contexts affect the available link bandwidth. To this end, we conduct a measurement study which contains 38 repeated trips along the same 5~km circular road in the campus of Seoul National University in April and May 2013. We measure the download throughput of video streaming from two different networks (3G and 4G LTE) with varying location, time, humidity, and speed. Our measurement results reveal that the humidity and location are the more important factors in the 3G network, while the speed, time, and location are the more important ones in the 4G LTE network to predict the available link bandwidth. We then propose an adaptive video streaming framework, MASERATI, where the information of environments and contexts is used to predict the available bandwidth. We demonstrate that MASERATI significantly improves the QoE of mobile streaming users in terms of the playout success rate, video quality, and stability, in comparison to DASH.