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.
Performance modeling of adaptive video streaming in wireless networks has mostly been considered for a single wireless link. @cite_35 the effect of an unreliable wireless channel is modelled by an i.i.d packet loss process, and the video coding rate and the packet size are optimized under retransmission-based error correction. @cite_32 and @cite_1 adaptive media playout and adaptive layered coding is addressed respectively. Both papers define a queuing model on a video frame level, assuming that the wireless channel results in a Poisson frame arrival process at the receiving terminal, a simplification that may be reasonable if the buffering at the receiver side is significant, and therefore packet level delays do not need to be taken into account.
@cite_35 @cite_1 @cite_32
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Video streaming is one of the killer applications for cellular communications. The MPEG-4 fine-granularity scalability video coding technique can adapt to bandwidth variation and random packet errors. In this paper, to explore the impacts of cellular channel characteristics on the tolerance of buffer performance and quality of service, a novel statistical model-based adaptive media playout (AMP) is proposed by utilizing the statistical assumptions of both arrival and departure processes for a better decision on the dynamic threshold adjustment and frame-rate adjustment. Based on third-generation cellular transmission environment, simulation results will demonstrate that as compared to other AMP schemes, the proposed AMP control provides better visual quality with lower complexity. We consider the design of a scheduling policy for video streaming in a wireless network formed by several users and helpers (e.g., base stations). In such networks, any user is typically in the range of multiple helpers. Hence, an efficient policy should allow the users to dynamically select the helper nodes to download from and determine adaptively the quality level of the requested video segment. In order to obtain a tractable formulation, we follow a “divide and conquer” approach. First, we formulate a network utility maximization (NUM) problem where the network utility function is a concave and component-wise nondecreasing function of the time-averaged users' requested video quality index, and maximization is subject to the stability of all queues in the system. Second, we solve the NUM problem by using a Lyapunov drift plus penalty approach, obtaining a dynamic adaptive scheme that decomposes into two building blocks: 1) adaptive video quality and helper selection (run at the user nodes); and 2) dynamic allocation of the helper-to-user transmission rates (run at the help nodes). Our solution provably achieves NUM optimality in a strong per-sample path sense (i.e., without assumptions of stationarity and ergodicity). Third, we observe that, since all queues in the system are stable, all requested video chunks shall be eventually delivered. Fourth, in order to translate the requested video quality into the effective video quality at the user playback, it is necessary that the chunks are delivered within their playback deadline. This requires that the largest delay among all queues at the helpers serving any given user is less than the pre-buffering time of that user at its streaming session startup phase. In order to achieve this condition with high probability, we propose an effective and decentralized (albeit heuristic) scheme to adaptively calculate the pre-buffering and re-buffering time at each user. In this way, the system is forced to work in the “smooth streaming regime,” i.e., in the regime of very small playback buffer underrun rate. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions of a network with densely deployed helper and user nodes, including user mobility, variable bit-rate video coding, and users joining or leaving the system at arbitrary times. Network variations in video streaming require sufficient data to be prebuffered at the client prior to playout. This receiver buffer prevents the display process from starvation in case of network congestion. Prebuffering, however, is also responsible for the major part of the delay between requesting a media stream and playing it at the receiver. We show how adaptive media playout can be employed to reduce the delay introduced by the receiver buffer while preserving the same resilience against buffer underflow as in non-adaptive media playout. For adaptive media playout we adjust the playout speed of the media packets depending on the condition of the channel and the current client buffer fullness. We employ a two-state Markov channel model to analyze the buffer underflow-delay trade-off for our adaptive playout strategy and show that for typical parameters the average end-to-end delay can be reduced by 1 to 2 seconds. Video on-demand streaming from Internet-based servers is becoming one of the most important services offered by wireless networks today. In order to improve the area spectral efficiency of video transmission in cellular systems, small cells heterogeneous architectures (e.g., femtocells, WiFi off-loading) are being proposed, such that video traffic to nomadic users can be handled by short-range links to the nearest small cell access points (referred to as "helpers"). As the helper deployment density increases, the backhaul capacity becomes the system bottleneck. In order to alleviate such bottleneck we propose a system where helpers with low-rate backhaul but high storage capacity cache popular video files. Files not available from helpers are transmitted by the cellular base station. We analyze the optimum way of assigning files to the helpers, in order to minimize the expected downloading time for files. We distinguish between the uncoded case (where only complete files are stored) and the coded case, where segments of Fountain-encoded versions of the video files are stored at helpers. We show that the uncoded optimum file assignment is NP-hard, and develop a greedy strategy that is provably within a factor 2 of the optimum. Further, for a special case we provide an efficient algorithm achieving a provably better approximation ratio of @math , where @math is the maximum number of helpers a user can be connected to. We also show that the coded optimum cache assignment problem is convex that can be further reduced to a linear program. We present numerical results comparing the proposed schemes.