Multicast Stream Builder Based Video Service Using Adaptive Bitrate Streaming and Content Delivery Networks

Abstract

Currently, the delivery of multimedia content like video and live programs, not only happens via cable networks and DTH (direct to home), but are increasingly moving towards IP (internet protocol) based delivery of multimedia content. Consumers today may seek for a personalized & interactive video watching experience. MPVDs (Multi-programming Video Distributors) are increasingly switching to IP based video or any multimedia content delivery, to build an immersive video viewership base. MVPDs provide both live and on demand video services to consumers based on both subscription and transactional business models. Even though MPVDs are gaining in market share and provide a large volume of video services today, there are still several challenges that they need to overcome such as network congestion due to the downstream network bandwidth, unpredictable network loads since in most cases the MPVDs do not control the network they deliver content. Also, improving the access to network bandwidth alone will not solve the problem of network congestion, since the “core†and “aggregate†networks usually have fixed bandwidth pipes. However, in spite of several advancements in multimedia content delivery technologies, delivering best-in class video quality over IP networks, especially for live video streaming, still presents a host of challenges. Significant amongst these challenges are network delays/jitters and packet losses due to network congestion. Across any multimedia content delivery pipeline, a multimedia content streaming losses occur in the network where the content is delivered from the CDN (content delivery network) edge caches to consumer devices viz. mobile phones, tablet form factors, Smart TVs and/or STBs.Moreover, it is much more tedious and cost prohibitive to perform regular network upgrades for bandwidth expansion.MPVDs (Multi-programming Video Distributors) are increasingly switching to IP based video or any multimedia content delivery, to build an immersive video viewership base.

However, in spite of several advancements in multimedia content delivery technologies, delivering best-in class video quality over IP networks, especially for live video streaming, still presents a host of challenges. Significant amongst these challenges are network delays/jitters and packet losses due to network congestion. Across any multimedia content delivery pipeline, a multimedia content streaming losses occur in the network where the content is delivered from the CDN (content delivery network) edge caches to consumer devices viz. mobile phones, tablet form factors, Smart TVs and/or STBs. Adaptive Bitrate Streaming (ABR) partially addresses the streaming challenges on networks with uncertain bandwidth. The core problem of preventing downstream network congestion due to an increased traffic in proportion to a number of users (streams increases proportionately with increase in user request) and consequently there is a reduction in available bandwidth. ABR is largely a mechanism that helps improving user perception of content quality, in networks with uncertain bandwidth by switching to lower bitrate streams smoothly but does not address the core problem of minimizing traffic congestion for live streaming. Further, unlike on demand streaming over IP, live streaming does not necessarily require dedicated unicast streams all across the delivery networks, typically 1 per user per device. This system of unicast delivery may be avoided. Currently, live streaming techniques such as ABR relies on HTTP as a streaming protocol. Though HTTP based streams can easily be delivered over CDN (Content Delivery Networks) through cache-replication, they render themselves difficult to be delivered over multicast (UDP based protocol).

So, this paper introduces a Big Data Analytics driven SDN based multicast based stream builder video service that monitors real time video quality of service associated with a bit rate traffic from a plurality of client devices, wherein the quality of service associated with the bit rate traffic are associated with a plurality of network topologies. Our system ensures that multicast delivery of ABR profiles be made available as closer to the user as possible, by polling the network conditions and user activities, and making the network components programmable to ensure the delivery. The state of the network conditions, ABR profiles being served to users, number of concurrent streams being delivered at any time are all fed back to the Big Data analytics system in real time. Intelligent real time insights from the Big data analytics platform are fed to the SDN controller ensures that video streams are fetched through the most optimal multicast route to ensure optimal video delivery to clients.In addition to this, this paper introduces a system to create/update existing multicast network rings to optimally route traffic ensuring better video QoS for subscribers. It also dynamically adapts to prevailing network conditions and active decisioning to construct optimal end to end multicast traffic throughput driven by the SDN controller.Thus the process helps to improve the quality of the video also minimize the network congestion problem when compared to the traditional video services provide method. The excellence of the system is evaluated with the help of experimental results and discussions. Thus the multicast stream based content delivery networks with bitrate profile live video transmission process obtained 97.8% accuracy when compared to the traditional Cloud-based Video Streaming Service (CVSS).