This paper studies the problem of serving multiple live video streams to several different clients from a single access point over unreliable wireless links, which is expected to be major a consumer of future wireless capacity. This problem involves two characteristics. On the streaming side, different video streams may generate variable-bit-rate traffic with different traffic patterns. On the network side, the wireless transmissions are unreliable, and the link qualities differ from client to client. In order to alleviate the above stochastic aspects of both video streams and link unreliability, each client typically buffers incoming packets before playing the video. The quality of the video playback subscribed to by each flow depends, among other factors, on both the delay of packets as well as their throughput. In this paper we characterize precisely the capacity of the wireless video server in terms of what combination of joint per-packet-delays and throughputs can be supported for the set of flows, as a function of the buffering delay introduced at the server. We also address how to schedule packets at the access point to satisfy the joint per-packet-delay-throughput performance measure. We test the designed policy on the traces of three movies. From our tests, it appears to outperform other policies by a large margin.
Hou el al @cite_13 have proposed a model that jointly considers the per-packet delay bound and the per-flow throughput requirement. This model has been extended to consider variable-bit-rate traffic @cite_15 , fading wireless channels @cite_1 @cite_11 , the mixture of real-time and non-real-time traffic @cite_14 , and multi-hop wireless transmissions @cite_17 . However, these studies assume that all flows are synchronized and generate packets at the same time, and the results depend critically on this assumption. Moreover, they assume that all flows start playback immediately without buffering any packets, which is a critical feature of the playback process. Dutta el al @cite_2 have studied serving video streams when receivers may buffer packets before playing them. They assume, however, that all packets are available at the server when the system starts, which is applicable to on-demand videos, but not to live videos.
@cite_14 @cite_1 @cite_17 @cite_2 @cite_15 @cite_13 @cite_11
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We study the optimal streaming of variable bit-rate (VBR) video over a random VBR channel. The goal of a streaming application is to enable the successful decoding of each video object before its displaying deadline is violated. Hence, we define the main performance metric of a streaming system as the probability of un-interrupted video presentation, or jitter-free probability. Previous literature has described solutions to estimate the jitter-free probability by assuming either independence in the encoded data process or simplistic channel models. In this work, we present a novel analytical framework, which requires only some basic statistical information of an arbitrary VBR channel, to bound the probability of jitter-free playout under the constraint of initial playout delay and receiver buffer size. Both the infinite and finite buffer cases are considered. This technique is then applied to investigate streaming over a wireless system modeled by an extended Gilbert channel with ARQ transmission control. Experimental results with MPEG-4 VBR encoded video demonstrates that the proposed analysis derives close bounds to the actual system performance. Finally, we show that the proposed analysis provides a theoretical foundation to quantify the tradeoffs between the initial playout delay, the receiver buffer size, and the jitter-free probability for a general class of VBR streaming over random VBR channels. Multimedia communications often require intramedia synchronization for video data to prevent potential playout discontinuity resulting from network delay variation (jitter) while still achieving satisfactory playout throughput. In this paper, we propose a neural network (NN) based intravideo synchronization mechanism, called the intelligent video smoother (IVS), operating at the application layer of the receiving end system. The IVS is composed of an NN traffic predictor, an NN window determinator, and a window-based playout smoothing algorithm. The NN traffic predictor employs an on-line-trained back-propagation neural network (BPNN) to periodically predict the characteristics of traffic modeled by a generic interrupted Bernoulli process (IBP) over a future fixed time period. With the predicted traffic characteristics, the NN window determinator determines the corresponding optimal window by means of an off-line-trained BPNN in an effort to achieve a maximum of the playout quality (Q) value. The window-based playout smoothing algorithm then dynamically adopts various playout rates according to the window and the number of packets in the buffer. Finally, we show that via simulation results and live video scenes, compared to two other playout approaches, IVS achieves high-throughput and low-discontinuity playout under a mixture of IBP arrivals. Video surveillance and similar real-time applications on wireless networks require increased reliability and high performance of the underlying transmission layer. Classical solutions, such as Reed-Solomon codes, increase the reliability, but typically have the negative side-effect of additional overall delays due to processing overheads. This paper describes the delay reduction achieved through online network coding approaches with a limit on the number of packets to be mixed before decoding and a systematic encoding structure. We use the inorder per packet delay as our key performance metric. This metric captures the elapsed time between (network) encoding RTP packets and completely decoding the packets in-order on the receiver side. Our solutions are implemented and evaluated on a point-to-point link between a Raspberry Pi device and a network (de)coding enabled software running on a regular PC. We find that our sliding window approach (more feedback) outperforms all other tested mechanisms in terms of per-+packet delay including the Reed-Solomon encoding employing the systematic approach, a random linear network coding approach, and our proposed on-the-fly network coding approach (which relies on less feedback).We show gains in order of magnitudes between our sliding window and the other approaches when we manage the redundancy transmission adaptively. This low per-packet delay and the inherent reliability of our schemes make these solutions particularly suitable for real-time multimedia delivery in contrast to other classical and network coding strategies. 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.