Adaptation engine for a streaming service based on MPEG-DASH

Although MPEG-Dynamic Adaptation Streaming over HTTP (MPEG-DASH) eliminates data packet loss, based on the rate adaptation strategy adopted by the user and the network conditions, still playback interruptions may not be avoided. In this paper an algorithm for rate adaption in MPEG-DASH is proposed that employs fuzzy logic to estimate the resolution of the next video segment that each client should obtain from the server. In this way, the buffering time at each client is kept above a target buffering time and thus buffer overflows and unnecessary bit rate fluctuations are avoided. Simulation results showed that the proposed rate adaptation strategy has a beneficial effect on the quality each user perceives.

The MPEG-Dynamic Adaptation Streaming over HTTP (MPEG-DASH) tries to improve the quality perceived by the user by allowing the user's centric access to different resolutions of the video content via the Hypertext Transfer Protocol (HTTP). However, although it eliminates data packet loss, depending on the rate adaptation strategy adopted by the user, as well as, the network conditions, there may be playback interruptions. In this paper a novel MPEG-DASH adaptation scheme, the FDASH, is proposed, which, by employing fuzzy logic to control the buffering time and the resolution achieves the following: a) distributes the best possible resolution of video segments, b) delivers undisrupted video playback as a result of buffer underflows at the client, c) avoids unnecessary changes of video resolution owing to frequent fluctuations of the available connection throughput. Simulation results showed that the FDASH in comparison with other schemes succeeds in various network scenarios in providing the client with the best possible video rates avoiding buffer underflows and frequent changes of video resolution.

International Journal of Computer Applications, 2018

Dynamic adaptive streaming via HTTP (DASH) has been widely disseminated over the Internet especially under the circumstances of the time varying network, which it is still the biggest challenge for providing smoothly video streaming with high quality. In DASH system, after the downloading process of a segment is completed, the player estimates the available network bandwidth by calculating the download throughput and adapting the video bitrate level based on its estimations. At client side, the DASH player uses an Adaptive Bitrate Algorithm (ABR) to choose the suitable bitrate for next segment based on current conditions. However, these adaptive algorithms discard the fact that the segment sizes greatly vary for a given video bitrate. Hence they may fail to predict the time needed for downloading the next segment. In this paper, an adaptive bitrate algorithm is proposed based on the video segment size as well as the network bandwidth estimation and the current buffer occupancy in order to accurately predict the time needed to download the next segment. Simulation results show that the proposed scheme is able to predict the download time. Also we compared the proposed scheme with other conventional schemes, we found that our proposed scheme outperforms others in

HTTP Adaptive Streaming (HAS) is quickly becoming the dominant type of video streaming in Over-The-Top multimedia services. HAS content is temporally segmented and each segment is offered in different video qualities to the client. It enables a video client to dynamically adapt the consumed video quality to match with the capabilities of the network and/or the client's device. As such, the use of HAS allows a service provider to offer video streaming over heterogeneous networks and to heterogeneous devices. Traditionally, the H.264/AVC video codec is used for encoding the HAS content: for each offered video quality, a separate AVC video file is encoded. Obviously, this leads to a considerable storage redundancy at the video server as each video is available in a multitude of qualities. The recent Scalable Video Codec (SVC) extension of H.264/AVC allows encoding a video into different quality layers: by dowloading one or more additional layers, the video quality can be improved. While this leads to an immediate reduction of required storage at the video server, the impact of using SVC-based HAS on the network and perceived quality by the user are less obvious. In this article, we characterize the performance of AVC-and SVCbased HAS in terms of perceived video quality, network load and client characteristics, with the goal of identifying advantages and disadvantages of both options. 1 Smooth Streaming -The Official Microsoft IIS Sitehttp://www.iis.net/download/SmoothStreaming

Lecture Notes in Computer Science, 2007

We propose a layered quality adaptation scheme for video streams to smooth the short-term oscillations induced by Additive Increase Multiplicative Decrease (AIMD) mechanisms, and eventually refine the perceptual video quality. The layered scheme utilizes receiver buffering, adapting the video quality along with long-term variations in the available bandwidth. The allocation of a new layer is based on explicit criteria that consider the available bandwidth, as well as the amount of buffering at the receiver. Consequently, the adaptation mechanism prevents wasteful layer changes that have an adverse effect on userperceived quality. In the sequel, we concentrate on the interactions of the layered approach with Scalable Streaming Video Protocol (SSVP). Exploiting performance measures related to the perceived quality of rate-adaptive video streams, we quantify the combination of SSVP rate control and receiverbuffered layered adaptation.

2007

The transfer of information over the wireless networks is emerging as a promising business model. In addition, new applications require the transfer of video content in real time. However, the unpredictable nature of wireless and mobile networks in terms of bandwidth, end-to-end delay and packet loss has tremendous impact on the transmission of video streams. In this paper, we adopt Network Adaptation Techniques applied together with Content Adaptation Techniques to achieve graceful performance degradation when network load increases and network conditions deteriorate. We present a new feedback mechanism that provides video adaptation to network parameters, working together with a fuzzy-based decision algorithm. Our preliminary performance evaluations indicate that our algorithm can finely adapt the video stream bit rate to the available bandwidth while providing fairness as well as high and stable objective quality of service.

Journal of Communications and Networks, 2013

Adaptive HTTP streaming has become a new trend to support adaptivity in video delivery. An HTTP streaming client needs to estimate exactly resource availability and resource demand. In this paper, we focus on the most important resource which is bandwidth. A new and general formulation for throughput estimation is presented taking into account previous values of instant throughput and round trip time. Besides, we introduce for the first time the use of bitrate estimation in HTTP streaming. The experiments show that our approach can effectively cope with drastic changes in connection throughput and video bitrate.

International Journal of Multimedia Data Engineering and Management, 2016

In this work, the authors present a novel bandwidth management solution for optimizing overall quality of experience (QoE) of multiple video streaming sessions. Instead of allocating bandwidth equally among competing flows, they propose to tailor the bandwidth allocation to both content complexity of requested video and playout buffer status of individual clients. The authors formulate the multi-client bandwidth allocation problem within the convex optimization framework, which is flexible enough to accommodate a wide variety of video quality metrics. Further, the authors present a practical architecture based on software defined networking (SDN) with two components: video quality monitoring and video quality optimization. Testbed-based experiments confirm that with quality-optimized allocation the network can support up to 75% more users at the same level of quality-of-experience (QoE) than conventional equal-rate allocations.

18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004.

The lack of end-to-end quality of service support in the current Internet has caused significant difficulties to ensuring playback continuity in video streaming applications. This study addresses this challenge by investigating a new adaptation algorithm to adjust the bit-rate of video data in response to the network bandwidth available to improve playback continuity. Unlike previous works, the proposed algorithm is transparent to the video client, requires no parameter tuning, and yet can outperform existing algorithms. This paper presents this algorithm, evaluates and compares its performance with the best algorithm currently available using extensive trace-driven simulations.

IEEE/ACM Transactions on Networking, 2018

Video streaming today accounts for up to 55% of mobile traffic. In this paper, we explore streaming videos encoded using Scalable Video Coding scheme (SVC) over highly variable bandwidth conditions such as cellular networks. SVC's unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop Deadline and Buffer Aware Bin Packing Algorithm (DBABP), a novel algorithm that solves the proposed optimization problem. Moreover, we show that DBABP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online DBABP) where several challenges are addressed including handling bandwidth prediction errors, and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared to the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today's mobile devices.