Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high quality and low delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity, model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the model using extensive simulations. We show that it allows fast, near optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than @math quality degradation compared to the best achievable performance.
Proposed rate adaptation methods for SVC are based on buffer content @cite_28 , transmission rate estimation @cite_25 @cite_27 , or both @cite_8 , with the advantage that detailed modeling of the network performance is not required. Low delay applications however can not build on buffer-content-based models. Results presented in the literature consider tens of seconds of playout delays. Similarly for low latency requirements, rate adaptation based on average transmission rate would be overly optimistic; it would result in queuing delays at the network nodes and late arrivals at the playout buffer. Therefore, in this paper we propose rate adaptation based on network performance modeling for low latency wireless applications.
@cite_28 @cite_27 @cite_25 @cite_8
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Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes and enhancing the quality of the video shown to the user. A bitrate that is too high leads to frequent video freezes (i.e., rebuffering), while a bitrate that is too low leads to poor video quality. Video providers segment the video into short chunks and encode each chunk at multiple bitrates. The video player adaptively chooses the bitrate of each chunk that is downloaded, possibly choosing different bitrates for successive chunks. While bitrate adaptation holds the key to a good quality of experience for the user, current video players use ad-hoc algorithms that are poorly understood. We formulate bitrate adaptation as a utility maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization techniques to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term O(1 V) of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, our algorithm does not require any prediction of available network bandwidth. We empirically validate our algorithm in a simulated network environment using an extensive collection of network traces. We show that our algorithm achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and for the evolving DASH standard for video transmission. Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS), which brings two new features. First, by using HTTP TCP, it leverages network-friendly TCP to achieve both firewall NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom in HAS design is that since the TCP throughput observed by a client would indicate the available network bandwidth, it could be used as a reliable reference for video bitrate selection. We argue that this is no longer true when HAS becomes a substantial fraction of the total network traffic. We show that when multiple HAS clients compete at a network bottleneck, the discrete nature of the video bitrates results in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to video bitrate oscillation and other undesirable behaviors that negatively impact the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation (where "probe" refers to trial increment of the data rate, instead of sending auxiliary piggybacking traffic), which is akin, but also orthogonal to the transport-layer TCP congestion control. We present PANDA - a client-side rate adaptation algorithm for HAS - as a practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75 without increasing the risk of buffer underrun. Today, video distribution platforms use adaptive video streaming to deliver the maximum Quality of Experience to a wide range of devices connected to the Internet through different access networks. Among the techniques employed to implement video adaptivity, the stream-switching over HTTP is getting a wide acceptance due to its deployment and implementation simplicity. Recently it has been shown that the client-side algorithms proposed so far generate an on-off traffic pattern that may lead to unfairness and underutilization when many video flows share a bottleneck. In this paper we propose ELASTIC (fEedback Linearization Adaptive STreamIng Controller), a client-side controller designed using feedback control theory that does not generate an on-off traffic pattern. By employing a controlled testbed, allowing bandwidth capacity and delays to be set, we compare ELASTIC with other client-side controllers proposed in the literature. In particular, we have checked to what extent the considered algorithms are able to: 1) fully utilize the bottleneck, 2) fairly share the bottleneck, 3) obtain a fair share when TCP greedy flows share the bottleneck with video flows. The obtained results show that ELASTIC achieves a very high fairness and is able to get the fair share when coexisting with TCP greedy flows. User-perceived quality-of-experience (QoE) is critical in Internet video applications as it impacts revenues for content providers and delivery systems. Given that there is little support in the network for optimizing such measures, bottlenecks could occur anywhere in the delivery system. Consequently, a robust bitrate adaptation algorithm in client-side players is critical to ensure good user experience. Previous studies have shown key limitations of state-of-art commercial solutions and proposed a range of heuristic fixes. Despite the emergence of several proposals, there is still a distinct lack of consensus on: (1) How best to design this client-side bitrate adaptation logic (e.g., use rate estimates vs. buffer occupancy); (2) How well specific classes of approaches will perform under diverse operating regimes (e.g., high throughput variability); or (3) How do they actually balance different QoE objectives (e.g., startup delay vs. rebuffering). To this end, this paper makes three key technical contributions. First, to bring some rigor to this space, we develop a principled control-theoretic model to reason about a broad spectrum of strategies. Second, we propose a novel model predictive control algorithm that can optimally combine throughput and buffer occupancy information to outperform traditional approaches. Third, we present a practical implementation in a reference video player to validate our approach using realistic trace-driven emulations.