Energy optimization for mobile video streaming via an aggregate model

Wireless video streaming on smartphones drains a significantly large fraction of battery energy, which is primarily consumed by wireless network interfaces for downloading unused data and repeatedly switching radio interface. In this paper, we propose an energy-efficient download scheduling algorithm for video streaming based on an aggregate model that utilizes user’s video viewing history to predict user behavior when watching a new video, thereby minimizing wasted energy when streaming over wireless network interfaces. The aggregate model is constructed by a personal retention model with users’ personal viewing history and the audience retention on crowd-sourced viewing history, which can accurately predict the user behavior of watching videos by balancing “user interest” and “video attractiveness”. We evaluate different users streaming multiple videos in various wireless environments and the results illustrate that the aggregate model can help reduce energy waste by 20 % on average. In addition, we also discuss implementation details and extensions, such as dynamically updating personal retention, balancing audience and personal retention, categorizing videos for accurate model.

[1]  Hsiao-Hwa Chen,et al.  Energy-Spectrum Efficiency Tradeoff for Video Streaming over Mobile Ad Hoc Networks , 2013, IEEE Journal on Selected Areas in Communications.

[2]  Qing Yang,et al.  LBVC: towards low-bandwidth video chat on smartphones , 2015, MMSys.

[3]  Insik Shin,et al.  GreenBag: Energy-Efficient Bandwidth Aggregation for Real-Time Streaming in Heterogeneous Mobile Wireless Networks , 2013, 2013 IEEE 34th Real-Time Systems Symposium.

[4]  Zhiwu Huang,et al.  Energy efficient video streaming over wireless networks with mobile-to-mobile cooperation , 2015, 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM).

[5]  Hossam S. Hassanein,et al.  Joint Chance-Constrained Predictive Resource Allocation for Energy-Efficient Video Streaming , 2016, IEEE Journal on Selected Areas in Communications.

[6]  Feng Qian,et al.  Profiling resource usage for mobile applications: a cross-layer approach , 2011, MobiSys '11.

[7]  Matti Siekkinen,et al.  Streaming over 3G and LTE: how to save smartphone energy in radio access network-friendly way , 2013, MoVid '13.

[8]  Chong-kwon Kim,et al.  ePF-DASH: Energy-efficient prefetching based dynamic adaptive streaming over HTTP , 2015, 2015 International Conference on Big Data and Smart Computing (BIGCOMP).

[9]  Gabriel-Miro Muntean,et al.  Energy consumption analysis of video streaming to Android mobile devices , 2012, 2012 IEEE Network Operations and Management Symposium.

[10]  Susmit Bagchi.  A fuzzy algorithm for dynamically adaptive multimedia streaming , 2011, TOMCCAP.

[11]  Sampath Rangarajan,et al.  MuVi: a multicast video delivery scheme for 4g cellular networks , 2012, Mobicom '12.

[12]  Matti Siekkinen,et al.  Modeling, Profiling, and Debugging the Energy Consumption of Mobile Devices , 2015, ACM Comput. Surv..

[13]  Yonggang Wen,et al.  CBM: Online Strategies on Cost-Aware Buffer Management for Mobile Video Streaming , 2014, IEEE Transactions on Multimedia.

[14]  Jason Flinn,et al.  Self-Tuning Wireless Network Power Management , 2005, Wirel. Networks.

[15]  甲藤 二郎,et al.  Energy-Efficient Video Streaming over Named Data Networking using Interest Aggregation and Playout Buffer Control (放送技術) , 2015 .

[16]  Marco Mellia,et al.  YouTube everywhere: impact of device and infrastructure synergies on user experience , 2011, IMC '11.

[17]  Giuseppe Anastasi,et al.  A Survey on Energy Efficiency in P2P Systems , 2015, ACM Comput. Surv..

[18]  Xin Li,et al.  GreenTube: power optimization for mobile videostreaming via dynamic cache management , 2012, ACM Multimedia.

[19]  George Varghese,et al.  RadioJockey: mining program execution to optimize cellular radio usage , 2012, Mobicom '12.

[20]  Torbjorn Einarsson,et al.  Dynamic adaptive HTTP streaming of live content , 2011, 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[21]  Ramachandran Ramjee,et al.  Bartendr: a practical approach to energy-aware cellular data scheduling , 2010, MobiCom.

[22]  Matti Siekkinen,et al.  Dissecting mobile video services: An energy consumption perspective , 2013, 2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[23]  Rajesh K. Gupta,et al.  CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces , 2006, MobiSys '06.

[24]  Guohong Cao,et al.  Energy-aware video streaming on smartphones , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[25]  Martin Reisslein,et al.  WVSNP-DASH: Name-Based Segmented Video Streaming , 2015, IEEE Transactions on Broadcasting.

[26]  Matti Siekkinen,et al.  Using crowd-sourced viewing statistics to save energy in wireless video streaming , 2013, MobiCom.

[27]  Songqing Chen,et al.  Delving into internet streaming media delivery: a quality and resource utilization perspective , 2006, IMC '06.

[28]  M. G. Michalos,et al.  Dynamic Adaptive Streaming over HTTP , 2012 .

[29]  Feng Qian,et al.  TOP: Tail Optimization Protocol For Cellular Radio Resource Allocation , 2010, The 18th IEEE International Conference on Network Protocols.

[30]  Konstantina Papagiannaki,et al.  Catnap: exploiting high bandwidth wireless interfaces to save energy for mobile devices , 2010, MobiSys '10.

[31]  Songqing Chen,et al.  PSM-throttling: Minimizing Energy Consumption for Bulk Data Communications in WLANs , 2007, 2007 IEEE International Conference on Network Protocols.

[32]  Hwangjun Song,et al.  An Energy-Efficient HTTP Adaptive Video Streaming With Networking Cost Constraint Over Heterogeneous Wireless Networks , 2015, IEEE Transactions on Multimedia.

[33]  Qing Yang,et al.  A Context-Aware Framework for Reducing Bandwidth Usage of Mobile Video Chats , 2016, IEEE Transactions on Multimedia.