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.
: With the emergence of HAS, which rides on top of TCP, packet loss is no longer a concern. Instead, the main source of quality degradation becomes compression and downsampling artifacts. There have been several on-going efforts trying to tackle the video quality optimization problem for HAS, all from different perspectives. Mehrotra and Zhao consider an approach based on rate-distortion optimization and scalable video coding (SVC) @cite_7 . They formulate the problem with the buffer constraint in a way similar to ours, and obtain a sub-optimal solution based on Lagrangian multiplier. When attempting to extend their solution from SVC to redundantly encoded multiple rate levels, they have noted that it yields incorrect answer as the rate-distortion curve was not necessarily convex any more. In contrast, our dynamic programming solution does not require convexity in the rate-quality relationship.
@cite_7
2079282346 2357930737 2077252365 2098033563
In this paper, we present a proxy-based solution for adapting the scalable video streams at the edge of a wireless network, which can respond quickly to highly dynamic wireless links. Our design adopts the scalable video coding (SVC) technique for lightweight rate adaptation at the edge. We derive a QoE model, i.e., rate-quality tradeoff model, that relates the maximum subjective quality under a given rate by choosing the optimal frame rate and quantization stepsize. The proxy iteratively allocates rates of different video streams to maximize a weighted sum of video qualities associated with different streams, based on the periodically observed link throughputs and the sending buffer status. Simulation studies show that our scheme consistently outperforms TFRC in terms of agility to track link qualities and overall quality of all streams. In addition, the proposed scheme supports differential services for different streams, and competes fairly with TCP flows. Although HTTP-based video streaming can easily penetrate firewalls and profit from Web caches, the underlying TCP may introduce large delays in case of a sudden capacity loss. To avoid an interruption of the video stream in such cases we propose the Backward-Shifted Coding (BSC). Based on Scalable Video Coding (SVC), BSC adds a time-shifted layer of redundancy to the video stream such that future frames are downloaded at any instant. This pre-fetched content maintains a fluent video stream even under highly variant network conditions and leads to high Quality of Experience (QoE). We characterize this QoE gain by analyzing initial buffering time, re-buffering time and content resolution using the Ballot theorem. The probability generating functions of the playback interruption and of the initial buffering latency are provided in closed form. We further compute the quasi-stationary distribution of the video quality, in order to compute the average quality, as well as temporal variability in video quality. Employing these analytic results to optimize QoE shows interesting trade-offs and video streaming at outstanding fluency. Wired and wireless data networks have witnessed a rapid proliferation of multimedia applications such as Internet video streaming, video conferencing, etc. A desirable key feature for multimedia transmission over multiuser environments is the ability of adapting rate and quality of video stream to different QoS conditions. The most efficient approach to address the scalability of multimedia applications is to encode video streams in compliance with the scalable video coding (SVC) standard, which is proposed as an extension to H.264 AVC standard. This paper addresses the rate control and bandwidth sharing for multimedia applications that are relying on SVC-encoded video streams. In previous studies, idealistic utility functions, mainly in the form of a staircase function, were used to cast the rate allocation for SVC-encoded streams as an optimization problem. These utility functions make the optimization problem non-convex and even non-differentiable, for which achieving optimality through dual-based approaches proves quite challenging. Towards this goal, we introduce an accurate analytical model of the utility function for SVC-encoded video streams. Using the abovementioned utility model and adopting the utility-proportional optimization approach, we come up with a convex formulation and propose a dual-based distributed algorithm for rate allocation of SVC-encoded streams. To the best of our knowledge, this is the first work that focuses on an accurate utility function modeling for SVC-encoded streams. Simulation experiments show that the proposed algorithm is quite efficient in achieving the convergence towards the global optimality. We derive an optimization framework for joint view and rate scalable coding of multi-view video content represented in the texture plus depth format. The optimization enables the sender to select the subset of coded views and their encoding rates such that the aggregate distortion over a continuum of synthesized views is minimized. We construct the view and rate embedded bitstream such that it delivers optimal performance simultaneously over a discrete set of transmission rates. In conjunction, we develop a user interaction model that characterizes the view selection actions of the client as a Markov chain over a discrete state-space. We exploit the model within the context of our optimization to compute user-action-driven coding strategies that aim at enhancing the client's performance in terms of latency and video quality. Our optimization outperforms the state-of-the-art H.264 SVC codec as well as a multi-view wavelet-based coder equipped with a uniform rate allocation strategy, across all scenarios studied in our experiments. Equally important, we can achieve an arbitrarily fine granularity of encoding bit rates, while providing a novel functionality of view embedded encoding, unlike the other encoding methods that we examined. Finally, we observe that the interactivity-aware coding delivers superior performance over conventional allocation techniques that do not anticipate the client's view selection actions in their operation.