Rubiks: Practical 360-Degree Streaming for Smartphones

The popularity of 360° videos has grown rapidly due to the immersive user experience. 360° videos are displayed as a panorama and the view automatically adapts with the head movement. Existing systems stream 360° videos in a similar way as regular videos, where all data of the panoramic view is transmitted. This is wasteful since a user only views a small portion of the 360° view. To save bandwidth, recent works propose the tile-based streaming, which divides the panoramic view to multiple smaller sized tiles and streams only the tiles within a user's field of view (FoV) predicted based on the recent head position. Interestingly, the tile-based streaming has only been simulated or implemented on desktops. We find that it cannot run in real-time even on the latest smartphone (e.g., Samsung S7, Samsung S8 and Huawei Mate 9) due to hardware and software limitations. Moreover, it results in significant video quality degradation due to head movement prediction error, which is hard to avoid. Motivated by these observations, we develop a novel tile-based layered approach to stream 360° content on smartphones to avoid bandwidth wastage while maintaining high video quality. Through real system experiments, we show our approach can achieve up to 69% improvement in user QoE and 49% in bandwidth savings over existing approaches. To the best of our knowledge, this is the first 360° streaming framework that takes into account the practical limitations of Android based smartphones.

[1]  Feng Qian,et al.  Optimizing 360 video delivery over cellular networks , 2016, ATC@MobiCom.

[2]  Yi Sun,et al.  CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction , 2016, SIGCOMM.

[3]  Ali C. Begen,et al.  Server-based traffic shaping for stabilizing oscillating adaptive streaming players , 2013, NOSSDAV '13.

[4]  Xinyu Zhang,et al.  POI360: Panoramic Mobile Video Telephony over LTE Cellular Networks , 2017, CoNEXT.

[5]  W.A.C. Fernando,et al.  Region of Interest Video Coding with Flexible Macroblock Ordering , 2006, First International Conference on Industrial and Information Systems.

[6]  Wei Song,et al.  Impact of Region-of-Interest Video Coding on Perceived Quality in Mobile Video , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[7]  Bruno Sinopoli,et al.  A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP , 2015, Comput. Commun. Rev..

[8]  Jian Wang,et al.  Error-resilient region-of-interest video coding , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Feng Qian,et al.  360° Innovations for Panoramic Video Streaming , 2017, HotNets.

[10]  Zhenhua Li,et al.  A Measurement Study of Oculus 360 Degree Video Streaming , 2017, MMSys.

[11]  Thomas Stockhammer,et al.  Dynamic adaptive streaming over HTTP --: standards and design principles , 2011, MMSys.

[12]  S. Grgic,et al.  Scalable video coding extension of H.264/AVC , 2012, Proceedings ELMAR-2012.

[13]  Christian Timmerer,et al.  Demo paper: Libdash - An open source software library for the MPEG-DASH standard , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[14]  Vyas Sekar,et al.  Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE , 2012, CoNEXT '12.

[15]  Zhou Wang,et al.  Video quality assessment based on structural distortion measurement , 2004, Signal Process. Image Commun..

[16]  Thomas Wiegand,et al.  Mobile Video Transmission Using Scalable Video Coding , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Peter Schelkens,et al.  HEVC-based video coding with lossless region of interest for telemedicine applications , 2013, 2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP).

[18]  Xin Liu,et al.  Shooting a moving target: Motion-prediction-based transmission for 360-degree videos , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[19]  Te-Yuan Huang,et al.  A buffer-based approach to rate adaptation: evidence from a large video streaming service , 2015, SIGCOMM 2015.

[20]  Hongzi Mao,et al.  Neural Adaptive Video Streaming with Pensieve , 2017, SIGCOMM.

[21]  Ofer Hadar,et al.  ROI adaptive scalable video coding for limited bandwidth wireless networks , 2010, 2010 IFIP Wireless Days.

[22]  Zhengguo Li,et al.  Region-of-Interest Based Resource Allocation for Conversational Video Communication of H.264/AVC , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Ali C. Begen,et al.  Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale , 2013, IEEE Journal on Selected Areas in Communications.

[24]  覃政 Google Cardboard:伟大的搅局者 , 2014 .

[25]  Madhukar Budagavi,et al.  HEVC Transform and Quantization , 2014, High Efficiency Video Coding.

[26]  Jens-Rainer Ohm,et al.  Advances in Scalable Video Coding , 2005, Proceedings of the IEEE.

[27]  Mohammad Hosseini,et al.  Adaptive 360 VR Video Streaming: Divide and Conquer , 2016, 2016 IEEE International Symposium on Multimedia (ISM).

[28]  Christian Timmerer,et al.  Towards Bandwidth Efficient Adaptive Streaming of Omnidirectional Video over HTTP: Design, Implementation, and Evaluation , 2017, MMSys.

[29]  N. Tsapatsoulis,et al.  Region of Interest Video Coding for Low bit-rate Transmission of Carotid Ultrasound Videos over 3G Wireless Networks , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.