Techniques for efficient stream of layered video in heterogeneous client environments

Abstract

Universal Multimedia Access (UMA) refers to accessing multimedia content over a wide range of client terminals and network capacities. Scalable coding is a very popular technique to enable UMA for video. Overhead introduced by the scalable coding approach limits the number of layers that can be stored for each video. Therefore some clients may be served the closest available quality than the best-fit quality. This is a major drawback of scalable coding from the enduser perspective. We propose to employ transcoding to tailor content exactly to the client's best-fit quality when the required layer is not stored. Inserting a transcoder in the server-client path introduces new challenges in deciding the layering structure (number of layers, bandwidth per layer) of a video. The optimal layering structure should be decided based on factors like total I/O bandwidth penalty incurred due to layering and transcoding effort required to service the "non-layered" versions. The solution to this problem is further complicated by practical issues like diverse popularity of video objects and resource availability. Another issue that we address in this paper is reducing WAN bandwidth penalty incurred due to transport and coding overhead inherent to scalable coding. This particular problem applies to all schemes that use layered encoding to broadcast video. We map the above mentioned problems onto a 0-1 multiple choice knapsack structure and propose an algorithm to find a nearoptimal solution. The uniqueness of our approach not only lies in the streaming model but also in the integrated manner in which we address a variety of issues put forth by layered coding.

An Optimal Adaptation Framework for Streaming Multiple Video Objects

“…In [7], joint transcoding of multiple video streams has been studied, which is to provide 1051-8215/$25.00 © 2008 IEEE a simple bit-allocation method for multiple video streams based on MPEG-2 TM5 using the motion activity information. In [10], the authors proposed a selective layering method to maximally save extraction time taken for bit stream layers of multiple video by considering CPU effort, I/O overhead and layering overhead. For content adaptation in streaming, some analytical frameworks have been studied to measure the transcoding overhead such as the required CPU power and transcoding time as transcoding costs [8]- [10].…”

“…In [10], the authors proposed a selective layering method to maximally save extraction time taken for bit stream layers of multiple video by considering CPU effort, I/O overhead and layering overhead. For content adaptation in streaming, some analytical frameworks have been studied to measure the transcoding overhead such as the required CPU power and transcoding time as transcoding costs [8]- [10]. These methods have mainly dealt with specific transcoding methods that depend upon their corresponding encoding formats.…”

“…The bit rate function can be approximated as with a series of weighted sampling points of the bit rate functions for (10) The transcoding cost function of can be approximated as with a series of weighted sampling points of (11) From (11), the sampling points of are required to be known a priori to approximate . However, at any PSNR sampling point for which its corresponding video stream exists, the transcoding cost is zero because the bit stream becomes a subject of not transcoding but selection.…”

Enhancing Scalability in On-Demand Video Streaming Services for P2P Systems

“…Video server faces scalability problem to a large extent with millions of users added to the community every year. Therefore, serving heterogeneous clients efficiently is still an unsolved problem at the servers [2].…”

User-Centric Energy-Efficient Multimedia Multicast/Broadcast Solutions: A Survey

“…This is achieved by means of transcoding to complement SVC for reduced bandwidth waste [99], adapting video transmission rate and FEC for multirate wireless network [100], bit rate allocation to layers and providing UEP [101], optimize frame rate allocation [102], rate allocation to video layers for QoE guarantee [103], and rate allocation among multiple video multicast sessions [104].…”