The popularity of social media has grown dramatically over the World Wide Web. In this paper, we analyze the video
popularity distribution of well-known social video websites (YouTube, Google Video, and the AOL Truveo Video Search
engine) and characterize their workload. We identify trends in the categories, lengths, and formats of those videos, as well
as characterize the evolution of those videos over time. We further provide an extensive analysis and comparison of video
content amongst the main regions of the world.
This paper presents a delivery framework for streaming media with advertisements and an associated pricing
model. The delivery model combines the benefits of periodic broadcasting and stream merging. The advertisements'
revenues are used to subsidize the price of the media content. The pricing is determined based on the total
ads' viewing time. Moreover, this paper presents an efficient ad allocation scheme and three modified scheduling
policies that are well suited to the proposed delivery framework. Furthermore, we study the effectiveness of the
delivery framework and various scheduling polices through extensive simulation in terms of numerous metrics,
including customer defection probability, average number of ads viewed per client, price, arrival rate, profit, and
The number of media streams that can be supported concurrently is highly constrained by the stringent requirements of real-time playback and high transfer rates. To address this problem, media delivery techniques, such as Batching and Stream Merging, utilize the multicast facility to increase resource sharing. The achieved resource sharing depends greatly on how the waiting requests are scheduled for service. Scheduling has been studied extensively when Batching is applied, but up to our knowledge, it has not been investigated in the context of stream merging techniques, which achieve much better resource sharing. In this study, we analyze scheduling when stream merging is employed and propose a simple, yet highly effective scheduling policy, called Minimum Cost First (MCF). MCF exploits the wide variation in stream lengths by favoring the requests that require the least cost. We present two alternative implementations of MCF: MCF-T and MCF-P. We compare various scheduling policies through extensive simulation and show that MCF achieves significant performance benefits in terms of both the number of requests that can be serviced concurrently and the average waiting time for service.