The carbon footprint of watching television, comparing digital terrestrial television with video-on-demand

Chandaria, Jigna; Hunter, Jeff; Williams, A.

doi:10.1109/issst.2011.5936908

Cited by 16 publications

(21 citation statements)

References 9 publications

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“…These costs were based on the running costs of servers [23] and current Intel [24] processors [25]. The emissions were calculated with the National Energy Foundation (NEF) carbon calculator for 2016 [26], and using the cheapest business rate for January 2017 of 10.5 pence per kWh for electricity [27].…”

Section: B Estimated Costs Of the Current Broadcast Systemmentioning

confidence: 99%

See 1 more Smart Citation

Spatially Adaptive TV Broadcast System: Hardware in the Loop Operational Analysis

Bagot

Beach

Nix

et al. 2018

IEEE Trans. on Broadcast.

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Section: B Estimated Costs Of the Current Broadcast Systemmentioning

confidence: 99%

“…Fully optimising the broadcast powers of the network is something that cannot easily be addressed during Manuscript received May 10, 2016; revised August 6, 2016, January 6, 2017 and January 20, 2017. Accepted January 23,2017. Figure 5 and related data has been reproduced from research previously presented in [1].…”

Section: Introductionmentioning

confidence: 99%

Spatially Adaptive TV Broadcast System: Hardware in the Loop Operational Analysis

Bagot

Beach

Nix

et al. 2018

IEEE Trans. on Broadcast.

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“…A 2011 BBC study [39] found that set-top boxes (the modems which deliver both cable TV and Internet connectivity) are on for 15.57 hours/day, but it did not address their usage time. A 2007 study found that in Europe, DSL modems are idle for 20 hours/day and in use for the remaining 4 hours/day [40].…”

Section: The Energy Intensity Of Cpe and Access Networkmentioning

confidence: 99%

“…Although this study is older, we use its assumptions. The study distinguishes between the on and idle state of modems, and we feel that the on-time of 24 hours/day from [40] better reflects reality than the 15.57 hours/day from the 2011 study [39]. With these assumptions, t On /t U se = 6.…”

Section: The Energy Intensity Of Cpe and Access Networkmentioning

confidence: 99%

The Energy Intensity of the Internet: Home and Access Networks

Coroamă

Schien

Preist

et al. 2014

Advances in Intelligent Systems and Computing

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Estimates of the energy intensity of the Internet diverge by several orders of magnitude. We present existing assessments and identify diverging definitions of the system boundary as the main reason for this large spread. The decision of whether or not to include end devices influences the result by 1-2 orders of magnitude. If end devices are excluded, customer premises equipment (CPE) and access networks have a dominant influence. Of less influence is the consideration of cooling equipment and other overhead, redundancy equipment, and the amplifiers in the optical fibers. We argue against the inclusion of end devices when assessing the energy intensity of the Internet, but in favor of including CPE, access networks, redundancy equipment, cooling and other overhead as well as optical fibers. We further show that the intensities of the metro and core network are best modeled as energy per data, while the intensity of CPE and access networks are best modeled as energy per time (i.e., power), making overall assessments challenging. The chapter concludes with a formula for the energy intensity of CPE and access networks. The formula is presented both in generic form as well as with concrete estimates of the average case to be used in quick assessments by practitioners. The following chapter develops a similar formula for the core and edge networks. Taken together, the two chapters provide an assessment method of the Internet's energy intensity that takes into account dierent modeling paradigms for dierent parts of the network. Abstract. Estimates of the energy intensity of the Internet diverge by several orders of magnitude. We present existing assessments and identify diverging definitions of the system boundary as the main reason for this large spread. The decision of whether or not to include end devices influences the result by 1-2 orders of magnitude. If end devices are excluded, customer premises equipment (CPE) and access networks have a dominant influence. Of less influence is the consideration of cooling equipment and other overhead, redundancy equipment, and the amplifiers in the optical fibers. We argue against the inclusion of end devices when assessing the energy intensity of the Internet, but in favor of including CPE, access networks, redundancy equipment, cooling and other overhead as well as optical fibers. We further show that the intensities of the metro and core network are best modeled as energy per data, while the intensity of CPE and access networks are best modeled as energy per time (i.e., power), making overall assessments challenging. The chapter concludes with a formula for the energy intensity of CPE and access networks. The formula is presented both in generic form as well as with concrete estimates of the average case to be used in quick assessments by practitioners. The following chapter develops a similar formula for the core and edge networks. Taken together, the two chapters provide an assessment method of the Internet's energy intensity that takes into account different ...

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“…This is because broadcast has fixed carbon costs that can be amortized over large audience sizes, whereas the carbon costs of streaming grows with each additional user [12]. Motivated by these observations, we ask whether the flexibility of on-demand viewing can be supported while still relying as much as possible on low-energy broadcast.…”

Section: Introductionmentioning

confidence: 99%

SCORE: Exploiting Global Broadcasts to Create Offline Personal Channels for On-Demand Access

Nencioni

Sastry

Tyson

et al. 2016

IEEE/ACM Trans. Networking

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The last 5 years have seen a dramatic shift in media distribution. For decades, TV and radio were solely provisioned using push-based broadcast technologies, forcing people to adhere to fixed schedules. The introduction of catch-up services, however, has now augmented such delivery with online pull-based alternatives. Typically, these allow users to fetch content for a limited period after initial broadcast, allowing users flexibility in accessing content. Whereas previous work has investigated both of these technologies, this paper explores and contrasts them, focusing on the network consequences of moving towards this multifaceted delivery model. Using traces from nearly 6 million users of BBC iPlayer, one of the largest catch-up TV services, we study this shift from push-to pull-based access. We propose a novel technique for unifying both push-and pull-based delivery: the Speculative Content Offloading and Recording Engine (SCORE). SCORE operates as a set-top box, which interacts with both broadcast push and online pull services. Whenever users wish to access media, it automatically switches between these distribution mechanisms in an attempt to optimize energy efficiency and network resource utilization. SCORE also can predict user viewing patterns, automatically recording certain shows from the broadcast interface. Evaluations using our BBC iPlayer traces show that, based on parameter settings, an oracle with complete knowledge of user consumption can save nearly 77% of the energy, and over 90% of the peak bandwidth, of pure IP streaming. Optimizing for energy consumption, SCORE can recover nearly half of both traffic and energy savings.

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The carbon footprint of watching television, comparing digital terrestrial television with video-on-demand

Cited by 16 publications

References 9 publications

Spatially Adaptive TV Broadcast System: Hardware in the Loop Operational Analysis

Spatially Adaptive TV Broadcast System: Hardware in the Loop Operational Analysis

The Energy Intensity of the Internet: Home and Access Networks

SCORE: Exploiting Global Broadcasts to Create Offline Personal Channels for On-Demand Access

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