Video streaming is one of the most popular and highest bandwidth consumers within the Internet today. Cloud's elastic and pay-per-use model offers viable solution to varying demands of heterogeneous viewers for large-scale video providers. Video providers are heavily exploiting cloud's elastic nature to cater the scalability and heterogeneity of video steaming related tasks. For instance, Netflix moved its whole infrastructure to Amazon cloud, and Twitch, one of the largest game streaming providers is owned by Amazon and now using Amazon's cloud. Video representations refer to multiple copies of same video transcoded in multiple bitrates, such as 240, 360, 720, 1080 etc. Viewers with varying bandwidth capacities are served with matching representations based on the available bandwidth to minimize buffering time and latency. However, video transcoding is a computation and communication intensive task, therefore, not all of the live videos are transcoded to different representations. For instance, Twitch transcodes only the video streams of premium member (which have 500+ regular viewers). All of the non-premium channels are broadcasted in source stream. A fundamental question therefore is: which channels should be considered to be transcoded to multiple representations to minimize the overall cloud leased resources cost and bandwidth, and to maximize user satisfaction. In this paper, we seek answer to this question by analyzing the impact of multiple representations on cost (based on leasing cloud resources), bandwidth, and Quality of Experience (QoE, measured in terms of user satisfaction). We use Twitch workload traces captured in 2015, to conduct the experimentation, and use latest real-world broadband and representation data rate statistics from Akamai and YouTube Live, and cost from Amazon EC2 and CloudFront to validate our results. Our analysis reveals that using cloud's resources to transcode channels with more than 40 average viewers per hour with a data rate of 720p or higher, leads to low cost and bandwidth consumption, and higher QoE, as compared to streaming source video without multiple representations.
[1]
Srinivasan Seshan,et al.
Practical, Real-time Centralized Control for CDN-based Live Video Delivery
,
2015,
SIGCOMM.
[2]
Gwendal Simon,et al.
YouTube live and Twitch: a tour of user-generated live streaming systems
,
2015,
MMSys.
[3]
Sherali Zeadally,et al.
A survey on Green communications using Adaptive Link Rate
,
2013,
Cluster Computing.
[4]
Bo Li,et al.
Coping With Heterogeneous Video Contributors and Viewers in Crowdsourced Live Streaming: A Cloud-Based Approach
,
2016,
IEEE Transactions on Multimedia.
[5]
Albert Y. Zomaya,et al.
Green Data Center Networks: Challenges and Opportunities
,
2013,
2013 11th International Conference on Frontiers of Information Technology.
[6]
Minghua Chen,et al.
CALMS: Cloud-assisted live media streaming for globalized demands with time/region diversities
,
2012,
2012 Proceedings IEEE INFOCOM.
[7]
Alberto Blanc,et al.
Transcoding live adaptive video streams at a massive scale in the cloud
,
2015,
MMSys.
[8]
Xiaofeng Wang,et al.
Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content
,
2015,
IEEE Transactions on Multimedia.
[9]
Alberto Blanc,et al.
Optimal set of video representations in adaptive streaming
,
2014,
MMSys '14.