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

A Hybrid of Adaptation and Dynamic Routing based on SDN for Improving QoE in HTTP Adaptive VBR Video Streaming

Recently, HTTP Adaptive Streaming HAS has received significant attention from both industry and academia based on its ability to enhancing media streaming services over the Internet. Recent research solutions that have tried to improve HAS by adaptation at the client side only may not be completely effective without interacting with routing decisions in the upper layers. In this paper, we address the aforementioned issue by proposing a dynamic bandwidth allocation and management architecture for streaming video flows to improve users satisfaction. We also introduce an initial cross layer hybrid method that combines quality adaptation of variable bitrate video streaming over the HTTP protocol at the client side and SDN based dynamical routing. This scheme is enabled by the Software Defined Networking architecture that is now being considered as an emerging paradigm that disassociates the forwarding process from the routing process. SDN brings flexibility and the ability to flexibly change routing solutions, in turn resulting in dynamically improving the services provided in the application layer. Our experimental results show that the proposed solution offers significantly higher overall bitrates as well as smoother viewing experience than existing methods.Comment: 14 pages, 17 figures, IJCSNS International Journal of Computer Science and Network Security, http://paper.ijcsns.org/07_book/201907/20190708.pd

Slight-Delay Shaped Variable Bit Rate (SD-SVBR) Technique for Video Transmission

The aim of this thesis is to present a new shaped Variable Bit Rate (VBR) for video transmission, which plays a crucial role in delivering video traffic over the Internet. This is due to the surge of video media applications over the Internet and the video typically has the characteristic of a highly bursty traffic, which leads to the Internet bandwidth fluctuation. This new shaped algorithm, referred to as Slight Delay - Shaped Variable Bit Rate (SD-SVBR), is aimed at controlling the video rate for video application transmission. It is designed based on the Shaped VBR (SVBR) algorithm and was implemented in the Network Simulator 2 (ns-2). SVBR algorithm is devised for real-time video applications and it has several limitations and weaknesses due to its embedded estimation or prediction processes. SVBR faces several problems, such as the occurrence of unwanted sharp decrease in data rate, buffer overflow, the existence of a low data rate, and the generation of a cyclical negative fluctuation. The new algorithm is capable of producing a high data rate and at the same time a better quantization parameter (QP) stability video sequence. In addition, the data rate is shaped efficiently to prevent unwanted sharp increment or decrement, and to avoid buffer overflow. To achieve the aim, SD-SVBR has three strategies, which are processing the next Group of Picture (GoP) video sequence and obtaining the QP-to-data rate list, dimensioning the data rate to a higher utilization of the leaky-bucket, and implementing a QP smoothing method by carefully measuring the effects of following the previous QP value. However, this algorithm has to be combined with a network feedback algorithm to produce a better overall video rate control. A combination of several video clips, which consisted of a varied video rate, has been used for the purpose of evaluating SD-SVBR performance. The results showed that SD-SVBR gains an impressive overall Peak Signal-to-Noise Ratio (PSNR) value. In addition, in almost all cases, it gains a high video rate but without buffer overflow, utilizes the buffer well, and interestingly, it is still able to obtain smoother QP fluctuation

A machine learning-based framework for preventing video freezes in HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) represents the dominant technology to deliver videos over the Internet, due to its ability to adapt the video quality to the available bandwidth. Despite that, HAS clients can still suffer from freezes in the video playout, the main factor influencing users' Quality of Experience (QoE). To reduce video freezes, we propose a network-based framework, where a network controller prioritizes the delivery of particular video segments to prevent freezes at the clients. This framework is based on OpenFlow, a widely adopted protocol to implement the software-defined networking principle. The main element of the controller is a Machine Learning (ML) engine based on the random undersampling boosting algorithm and fuzzy logic, which can detect when a client is close to a freeze and drive the network prioritization to avoid it. This decision is based on measurements collected from the network nodes only, without any knowledge on the streamed videos or on the clients' characteristics. In this paper, we detail the design of the proposed ML-based framework and compare its performance with other benchmarking HAS solutions, under various video streaming scenarios. Particularly, we show through extensive experimentation that the proposed approach can reduce video freezes and freeze time with about 65% and 45% respectively, when compared to benchmarking algorithms. These results represent a major improvement for the QoE of the users watching multimedia content online