Internet Video

Even though a key driver for Information-Centric Networking (ICN) has been the rise in Internet video traffic, there has been surprisingly little work on analyzing the interplay between ICN and video-which ICN caching strategies work well on video workloads and how ICN helps improve video-centric quality of experience (QoE). In this work, we bridge this disconnect with a tracedriven study using 196M video requests from over 16M users on a country-wide topology with 80K routers. We evaluate a broad space of content replacement (e.g., LRU, LFU, FIFO) and content placement (e.g., leave a copy everywhere, probabilistic) strategies over a range of cache sizes. We highlight four key findings: (1) the best placement and replacement strategies depend on the cache size and vary across improvement metrics; that said, LFU+probabilistic caching [37] is a close-to-optimal strategy overall; (2) video workloads show considerable caching-related benefits (e.g., ≥ 10% traffic reduction) only with very large cache sizes (≥ 100GB); (3) the improvement in video QoE is low (≤ 12%) if the content provider already has a substantial geographical presence; and (4) caches in the middle and the edge of the network, requests from highly populated regions and without content servers, and requests for popular content contribute most to the overall ICN-induced improvements in video QoE.

With ever more popularity of video web-publishing, many popular contents are being mirrored, reformatted, modi ed and republished, resulting in excessive content duplication. While such redundancy provides fault tolerance for continuous availability of information, it could potentially create problems for multimedia search engines in that the search results for a given query might become repetitious, and cluttered with a large number of duplicates. As such, developing techniques for detecting similarity and duplication is important t o m ultimedia search engines. In addition, content providers might b e i n terested in identifying duplicates of their content for legal, contractual or other business related reasons. In this paper, we propose an e cient algorithm called video signature to detect similar video sequences for large databases such as the web. The idea is to rst form a signature" for each video sequence by selecting a small number of its frames that are most similar to a number of randomly chosen seed images. Then the similarity b e t ween any t wo video sequences can be reliably estimated by comparing their respective signatures. Using this method, we a c hieve 85 recall and precision ratios on a test database of 377 video sequences. As a proof of concept, we h a ve applied our proposed algorithm to a collection of 1800 hours of video corresponding to around 45000 clips from the web. Our results indicate that, on average, every video in our collection from the web has around ve similar copies.

Dado el significativo aumento en los últimos cinco años de la difusión de contenidos audiovisuales a través de la web, se presenta en este artículo un estudio descriptivo y clasificatorio sobre una amplia muestra de iniciativas audiovisuales cuyo acceso se realiza mediante la World Wide Web. Este estudio tiene como objetivo general impulsar un debate acerca de las denominaciones de estos incipientes medios, así como su tipificación y caracterización, de manera que proporcione un universo ordenado del fenómeno WebTV. Se realiza un análisis de formatos y contenidos basándonos en técnicas cuantitativas, cuyo resultado será la proposición de una tipología de categorización. Dichos formatos y contenidos serán estudiados bajo tres variables fundamentales: “Contenidos”, “Origen” y “Dominio .tv”. “Contenidos” nos ayudará a delimitar las líneas programáticas de nuestra muestra objeto de estudio; “Origen” precisará la raíz o procedencia del caso de estudio (WebTV nativa o WebTV representativa de un medio convencional) y “Dominio .tv” concretará la proporción de casos de estudio alojados con el dominio .tv. Los resultados obtenidos ofrecen a investigadores y profesionales una amplia descripción de los modelos adoptados actualmente en el panorama del vídeo y la televisión en la web.