Desain dan Implementasi Live Streaming Televisi Menggunakan Adaptive H264encoding

Teknologi Informasi yang paling luas penyebarannya adalah Televisi, dengan kemajuan teknologi sarana penyiaran Televisi tidak terbatas lagi ke TV broadcast menggunakan teknologi radio di gelombang khusus seperti saat ini, penyiaran TV telah menyebar ke sarana yang lain termasuk internet. Banyak teknologi yang bisa digunakan di internet, tetapi kandidat yang paling kuat adalah video streaming. Untuk aplikasi real-time atau live seperti kampanye atau siaran pengumuman pemerintah dll, teknologi video streaming yang digunakan adalah teknologi video streaming khusus yang disebut dengan live streaming.Teknologi Live Streaming hampir sama dengan video streaming, hanya saja data yang digunakan langsung bersumber dari televisi atau kamera yang bersifat real time. Live Streaming memerlukan proses live encoding dan minimum buffering, sedangkan di sisi lain diharapkan delay seminimal mungkin. Masalah selanjutnya adalah keterbatasan bandwidth. Jaringan komputer yang digunakan untuk melewatkan berbagai aplikasi akan digunakan juga sebagai media streaming yang membutuhkan bitrate cukup tinggi. Proses ini akan menyebabkan beban jaringan bertambah sehingga service yang ada tidak dapat berjalan dengan baik (terganggu). Pada penelitian ini difokuskan pada proses live streaming H264 dengan metode transmisi multicast dengan ditambahkan sebuah program adaptive streaming. Codec H264 dipilih karena performansinya yang cukup baik pada level bitrate yang lebih rendah. Sistem multicast digunakan untuk mengatasi masalah keterbatasan bandwidth yang digunakan dalam streaming. Adaptive streaming digunakan untuk menyesuaikan bitrate dengan kondisi trafik pada jaringan. Didapatkan nilai PSNR 36,58 dB untuk bitrate 500kbps dan 31,42 dB untuk bitrate 200kbps yang masih berada diatas threshold ITU 20dB dengan MOS 3,4 untuk 50 responden, sistem adaptive menyebabkan berkurangnya paket loss dari 1,53% menjadi 0,46%, bandwitdh stream unucast 1698kbps untuk multicast 558kbps

Objective assessment of region of interest-aware adaptive multimedia streaming quality

Adaptive multimedia streaming relies on controlled adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality

Streaming Video over HTTP with Consistent Quality

In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.Comment: Refined version submitted to ACM Multimedia Systems Conference (MMSys), 201

Design and evaluation of a DASH-compliant second screen video player for live events in mobile scenarios

The huge diffusion of mobile devices is rapidly changing the way multimedia content is consumed. Mobile devices are often used as a second screen, providing complementary information on the content shown on the primary screen, as different camera angles in case of a sport event. The introduction of multiple camera angles poses many challenges with respect to guaranteeing a high Quality of Experience to the end user, especially when the live aspect, different devices and highly variable network conditions typical of mobile environments come into play. Due to the ability of HTTP Adaptive Streaming (HAS) protocols to dynamically adapt to bandwidth fluctuations, they are especially suited for the delivery of multimedia content in mobile environments. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Recently, a standardized solution has been proposed by the MPEG consortium, called Dynamic Adaptive Streaming over HTTP (DASH). We present in this paper a DASH-compliant iOS video player designed to support research on rate adaptation heuristics for live second screen scenarios in mobile environments. The video player allows to monitor the battery consumption and CPU usage of the mobile device and to provide this information to the heuristic. Live and Video-on-Demand streaming scenarios and real-time multi-video switching are supported as well. Quantitative results based on real 3G traces are reported on how the developed prototype has been used to benchmark two existing heuristics and to analyse the main aspects affecting battery lifetime in mobile video streaming