Self-tuning wireless network power management
Proceedings of the 9th annual international conference on Mobile computing and networking
CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces
Proceedings of the 4th international conference on Mobile systems, applications and services
Delving into internet streaming media delivery: a quality and resource utilization perspective
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Youtube traffic characterization: a view from the edge
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Multimedia streaming via TCP: An analytic performance study
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Energy consumption in mobile phones: a measurement study and implications for network applications
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Bartendr: a practical approach to energy-aware cellular data scheduling
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Characterizing radio resource allocation for 3G networks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
A fuzzy algorithm for dynamically adaptive multimedia streaming
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Dynamic adaptive streaming over HTTP --: standards and design principles
MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems
Profiling resource usage for mobile applications: a cross-layer approach
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
TOP: Tail Optimization Protocol For Cellular Radio Resource Allocation
ICNP '10 Proceedings of the The 18th IEEE International Conference on Network Protocols
YouTube everywhere: impact of device and infrastructure synergies on user experience
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Network characteristics of video streaming traffic
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
A close examination of performance and power characteristics of 4G LTE networks
Proceedings of the 10th international conference on Mobile systems, applications, and services
RadioJockey: mining program execution to optimize cellular radio usage
Proceedings of the 18th annual international conference on Mobile computing and networking
GreenTube: power optimization for mobile videostreaming via dynamic cache management
Proceedings of the 20th ACM international conference on Multimedia
Streaming over 3G and LTE: how to save smartphone energy in radio access network-friendly way
Proceedings of the 5th Workshop on Mobile Video
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Video streaming on smartphones is one of the most popular but also most energy hungry services today. Using mobile video services results in two contradictory sources of energy waste for smartphones: i) energy waste because of excessively aggressive prefetching of content that the user will not watch because of abandoning the session, and ii) excessive amount of tail energy, which is energy wasted by keeping the wireless interface powered on after receiving a chunk of content; this is caused by prefetching chunks that are too small. To remedy this, we propose a novel download scheduling algorithm based on crowd-sourced video viewing statistics. Our algorithm judiciously evaluates the probability of a user interrupting a video viewing in order to perform the right amount of prefetching. In this way, the algorithm balances the amount of the two above-mentioned kinds of energy waste. By simulations, we show that our scheduler cuts the energy waste to half compared to existing download strategies. We have also developed an Android prototype that implements the download scheduler together with a novel downloader that speeds up the download by exploiting the Fast Start technique. The prototype exhibits the desired properties of the scheduler, and its faster downloading mechanism yields further energy savings of up to 80% compared to the default Android YouTube app.